From e3ffb4d6391ad4b782e6ac5dcf72a8b69210cf57 Mon Sep 17 00:00:00 2001 From: Liwei Dong Date: Thu, 19 Jan 2023 07:44:12 +0800 Subject: [PATCH 1/5] README update. --- README.md | 171 +++++++++- audioflux/utils/sample_data/demo_guitar.wav | Bin 0 -> 1338890 bytes audioflux/utils/sample_data/demo_guitar2.wav | Bin 0 -> 429296 bytes audioflux/utils/sample_data/demo_guitar3.wav | Bin 0 -> 630092 bytes audioflux/utils/sample_data/demo_voice.wav | Bin 0 -> 515880 bytes docs/examples.md | 339 +++++++++++++++++++ docs/installing.md | 46 +++ image/demo_cwt.png | Bin 0 -> 48231 bytes image/demo_mel.png | Bin 0 -> 22170 bytes image/demo_mfcc.png | Bin 0 -> 14552 bytes image/demo_synsq.png | Bin 0 -> 38650 bytes image/demos/demo_chroma_cqt.png | Bin 0 -> 30270 bytes image/demos/demo_cqt.png | Bin 0 -> 32997 bytes image/demos/demo_cwt.png | Bin 0 -> 205704 bytes image/demos/demo_feature.png | Bin 0 -> 168308 bytes image/demos/demo_hpss.png | Bin 0 -> 165414 bytes image/demos/demo_hpss1.png | Bin 0 -> 26676 bytes image/demos/demo_hpss2.png | Bin 0 -> 136083 bytes image/demos/demo_onset.png | Bin 0 -> 177482 bytes image/demos/demo_pitch.png | Bin 0 -> 33245 bytes 20 files changed, 546 insertions(+), 10 deletions(-) create mode 100644 audioflux/utils/sample_data/demo_guitar.wav create mode 100644 audioflux/utils/sample_data/demo_guitar2.wav create mode 100644 audioflux/utils/sample_data/demo_guitar3.wav create mode 100644 audioflux/utils/sample_data/demo_voice.wav create mode 100644 docs/examples.md create mode 100644 docs/installing.md create mode 100644 image/demo_cwt.png create mode 100644 image/demo_mel.png create mode 100644 image/demo_mfcc.png create mode 100644 image/demo_synsq.png create mode 100644 image/demos/demo_chroma_cqt.png create mode 100644 image/demos/demo_cqt.png create mode 100644 image/demos/demo_cwt.png create mode 100644 image/demos/demo_feature.png create mode 100644 image/demos/demo_hpss.png create mode 100644 image/demos/demo_hpss1.png create mode 100644 image/demos/demo_hpss2.png create mode 100644 image/demos/demo_onset.png create mode 100644 image/demos/demo_pitch.png diff --git a/README.md b/README.md index cd12252..a66c15a 100644 --- a/README.md +++ b/README.md @@ -36,6 +36,29 @@ A library for audio and music analysis, feature extraction. Can be used for deep learning, pattern recognition, signal processing, bioinformatics, statistics, finance, etc +# Table of Contents + +- [Overview](#overview) + - [Description](#description) + - [Functionality](#functionality) + - [transform](#1-transform) + - [feature](#2-feature) + - [mir](#3-mir) +- [Quickstart](#quickstart) + - [Mel & MFCC](#mel--mfcc) + - [CWT & Synchrosqueezing](#cwt--synchrosqueezing) + - [More examples](#more-examples) +- [Installation](#installation) + - [Python Package Intsall](#python-package-intsall) + - [iOS build](#ios-build) + - [Android build](#android-build) + - [Compiling from source](#compiling-from-source) +- [Documentation](#documentation) +- [Contributing](#contributing) +- [Citing](#citing) +- [License](#license) + + ## Overview ### Description @@ -109,10 +132,126 @@ The mir module contains the following algorithms: - `onset` - Spectrum flux, novelty, etc algorithm. - `hpss` - Median filtering, NMF algorithm. + +## Quickstart + +### Mel & Mfcc + +```python +# Feature extraction example +import numpy as np +import audioflux as af +import matplotlib.pyplot as plt +from audioflux.display import fill_spec +from audioflux.type import SpectralFilterBankScaleType + +# Get a 220Hz's audio file path +sample_path = af.utils.sample_path('220') + +# Read audio data and sample rate +audio_arr, sr = af.read(sample_path) + +# Extract mel spectrogram +bft_obj = af.BFT(num=128, radix2_exp=12, samplate=sr, + scale_type=SpectralFilterBankScaleType.MEL) +spec_arr = bft_obj.bft(audio_arr) +spec_arr = np.abs(spec_arr) + +# Create XXCC object and extract mfcc +xxcc_obj = af.XXCC(bft_obj.num) +xxcc_obj.set_time_length(time_length=spec_arr.shape[1]) +mfcc_arr = xxcc_obj.xxcc(spec_arr) + +audio_len = audio_arr.shape[0] +fig, ax = plt.subplots() +img = fill_spec(spec_arr, axes=ax, + x_coords=bft_obj.x_coords(audio_len), + y_coords=bft_obj.y_coords(), + x_axis='time', y_axis='log', + title='Mel Spectrogram') +fig.colorbar(img, ax=ax) + +fig, ax = plt.subplots() +img = fill_spec(mfcc_arr, axes=ax, + x_coords=bft_obj.x_coords(audio_len), x_axis='time', + title='MFCC') +fig.colorbar(img, ax=ax) + +plt.show() +``` + + + +### CWT & Synchrosqueezing + +```python +# Feature extraction example +import numpy as np +import audioflux as af +import matplotlib.pyplot as plt +from audioflux.display import fill_spec +from audioflux.type import SpectralFilterBankScaleType, WaveletContinueType +from audioflux.utils import note_to_hz + +# Get a 220Hz's audio file path +sample_path = af.utils.sample_path('220') + +# Read audio data and sample rate +audio_arr, sr = af.read(sample_path) +audio_arr = audio_arr[:4096] + +cwt_obj = af.CWT(num=84, radix2_exp=12, samplate=sr, low_fre=note_to_hz('C1'), + bin_per_octave=12, wavelet_type=WaveletContinueType.MORSE, + scale_type=SpectralFilterBankScaleType.OCTAVE) + +cwt_spec_arr = cwt_obj.cwt(audio_arr) + +synsq_obj = af.Synsq(num=cwt_obj.num, + radix2_exp=cwt_obj.radix2_exp, + samplate=cwt_obj.samplate) + +synsq_arr = synsq_obj.synsq(cwt_spec_arr, + filter_bank_type=cwt_obj.scale_type, + fre_arr=cwt_obj.get_fre_band_arr()) + +# Show CWT +fig, ax = plt.subplots(figsize=(7,4)) +img = fill_spec(np.abs(cwt_spec_arr), axes=ax, + x_coords=cwt_obj.x_coords(), + y_coords=cwt_obj.y_coords(), + x_axis='time', y_axis='log', + title='CWT') +fig.colorbar(img, ax=ax) +# Show Synsq +fig, ax = plt.subplots(figsize=(7,4)) +img = fill_spec(np.abs(synsq_arr), axes=ax, + x_coords=cwt_obj.x_coords(), + y_coords=cwt_obj.y_coords(), + x_axis='time', y_axis='log', + title='Synsq') +fig.colorbar(img, ax=ax) + +plt.show() +``` + + + + +### Other examples + +- [CQT & Chroma](docs/examples.md#cqt--chroma) +- [Wavelet Type](docs/examples.md#wavelet-type) +- [Spectral Features](docs/examples.md#spectral-feature) +- [Pitch Estimate](docs/examples.md#pitch-estimate) +- [Onset Detection](docs/examples.md#onset-detection) +- [Harmonic-percussive source separation](docs/examples.md#harmonic-percussive-source-separation) + +More example scripts are provided in the [Documentation](https://audioflux.top/) section. + ## Installation ![language](https://img.shields.io/badge/platform-iOS%20|%20android%20|%20macOS%20|%20linux%20|%20windows%20-lyellow.svg) -The library is cross-platform and currently supports Linux, macOS, Windows, iOS and Android systems. +The library is cross-platform and currently supports Linux, macOS, Windows, iOS and Android systems. ### Python Package Intsall @@ -122,23 +261,19 @@ Using PyPI: $ pip install audioflux ``` -Using Anaconda: + -Building from source: - -``` -$ python setup.py build -$ python setup.py install -``` + ### iOS build + To compile iOS on a Mac, Xcode Command Line Tools must exist in the system: - Install the full Xcode package @@ -156,6 +291,7 @@ $ ./build_iOS.sh Build and compile successfully, the project build compilation results are in the **`build`** folder ### Android build + The current system development environment needs to be installed [**android NDK**](https://developer.android.com/ndk), ndk version>=16,after installation, set the environment variable ndk path. For example, ndk installation path is `~/Android/android-ndk-r16b`: @@ -175,6 +311,13 @@ $ ./build_android.sh Build and compile successfully, the project build compilation results are in the **`build`** folder + +### Compiling from source + +For Linux, macOS, Windows systems. Read installation instructions: + +* [docs/installing.md](docs/installing.md) + ## Documentation Documentation of the package can be found online: @@ -186,7 +329,15 @@ We are more than happy to collaborate and receive your contributions to **`audio You are also more than welcome to suggest any improvements, including proposals for need help, find a bug, have a feature request, ask a general question, new algorithms. Open an issue - + +## Citing + +If you want to cite **`audioFlux`** in a scholarly work, there are two ways to do it. + +- If you are using the library for your work, for the sake of reproducibility, please cite + the version you used as indexed at Zenodo: + + [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7548289.svg)](https://doi.org/10.5281/zenodo.7548289) ## License audioFlux project is available MIT License. diff --git a/audioflux/utils/sample_data/demo_guitar.wav b/audioflux/utils/sample_data/demo_guitar.wav new file mode 100644 index 0000000000000000000000000000000000000000..b4c36e45ec92cf60818aa8b471262a775d94f5a4 GIT binary patch literal 1338890 zcmXt>1(;RU*T&Dice;m37`jUu1Orq;M5IAVTBJilP(T4iluk)W0YwxkDUlYGlty}h zVY>6i`F`)2|M%?Y#+-BRK6|hAuH0*%>GDpSHuf=*#`|yfXgzfF#PVT=Vc2NKbiNcB zhTRA=h76oIu+->C!)4kHf5Y1dGJ^T^;+w}zGvm!0_l;Pnfc~D^QgJqTxTvf7n=*r zh30qW_vTh}pLxlA#+?%A{W14BPdjDqHn(tgqxplm)BN2$Zk{u*nD@;my#D2mr_IBR 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z=@_@TJ1c)i3*?sOQy>QUtMX*xax0!uwuy<|ZfYxk<370%bcPwXmVhTv`Zk^4x-()j zf5gX{4;mag8EpIBq99x^> zN#mB6Q6^jsnXo(=_t1{Ip;O~n+Xq3^5PY|(>AgK@0yQa%Mjt< zWNQw*NhXSElQ^uMRv!AjF!e ztm7CQh4(U`=e2XkpZK4kDhwu?1%b3;jKq9q?wstJn3!kIlk|ot;gmz z|H8~E{e;PB{iM|?{fNYDuQ#6350{+TnKSyHZF$=pEfh|t`kl?>8Of6;XOb7vnQPRzT;on{x6*Jsngzj%Bi#OKIM~V yo_5A3XWw?}N6-A|>AyaE$=}bu=lFNbo_PANo%!ig&Nyx6ttXuLUKTg6zyAj_A>MNU literal 0 HcmV?d00001 diff --git a/docs/examples.md b/docs/examples.md new file mode 100644 index 0000000..45867d4 --- /dev/null +++ b/docs/examples.md @@ -0,0 +1,339 @@ +# Examples + + + +- [CQT & Chroma](#cqt--chroma) +- [Wavelet Type](#wavelet-type) +- [Spectral Features](#spectral-feature) +- [Pitch Estimate](#pitch-estimate) +- [Onset Detection](#onset-detection) +- [Harmonic-percussive source separation](#harmonic-percussive-source-separation) + + +## CQT & Chroma + +```python +import numpy as np +import audioflux as af + +# Get a 220Hz's audio file path +sample_path = af.utils.sample_path('demo_guitar2') + +# Read audio data and sample rate +audio_arr, sr = af.read(sample_path) + +# Create CQT object +cqt_obj = af.CQT(num=84, samplate=sr) + +# Extract CQT and Chroma_cqt +cqt_arr = cqt_obj.cqt(audio_arr) +chroma_cqt_arr = cqt_obj.chroma(cqt_arr) + +# Display +import matplotlib.pyplot as plt +from audioflux.display import fill_spec +audio_len = audio_arr.shape[0] + +# Display CQT +fig, ax = plt.subplots() +img = fill_spec(np.abs(cqt_arr), axes=ax, + x_coords=cqt_obj.x_coords(audio_len), x_axis='time', + y_coords=cqt_obj.y_coords(), y_axis='log', + title='CQT') +fig.colorbar(img, ax=ax) + +# Display Chroma_cqt +fig, ax = plt.subplots() +img = fill_spec(chroma_cqt_arr, axes=ax, + x_coords=cqt_obj.x_coords(audio_len), + x_axis='time', y_axis='chroma', + title='Chroma-CQT') +fig.colorbar(img, ax=ax) + +# Show plot +plt.show() +``` + + + + + +## Wavelet Type + +```python +import audioflux as af +import numpy as np +from audioflux.type import SpectralFilterBankScaleType, WaveletContinueType +from audioflux.utils import note_to_hz +import matplotlib.pyplot as plt +from audioflux.display import fill_spec +from audioflux.display import fill_wave + +audio_path = af.utils.sample_path('220') +audio_arr, sr = af.read(audio_path) +# CWT can only input fft_length data +# For radix2_exp=12, then fft_length=4096 +audio_arr = audio_arr[:4096] + +# Create CWT object of Octave and Extract spectrogram +obj = af.CWT(num=84, radix2_exp=12, samplate=sr, + low_fre=note_to_hz('C1'), bin_per_octave=12, + wavelet_type=WaveletContinueType.MORLET, + scale_type=SpectralFilterBankScaleType.OCTAVE) +morlet_spec_arr = obj.cwt(audio_arr) +morlet_spec_arr = np.abs(morlet_spec_arr) + +obj = af.CWT(num=84, radix2_exp=12, samplate=sr, + low_fre=note_to_hz('C1'), bin_per_octave=12, + wavelet_type=WaveletContinueType.MORSE, + scale_type=SpectralFilterBankScaleType.OCTAVE) +morse_spec_arr = obj.cwt(audio_arr) +morse_spec_arr = np.abs(morse_spec_arr) + +obj = af.CWT(num=84, radix2_exp=12, samplate=sr, + low_fre=note_to_hz('C1'), bin_per_octave=12, + wavelet_type=WaveletContinueType.PAUL, + scale_type=SpectralFilterBankScaleType.OCTAVE) +paul_spec_arr = obj.cwt(audio_arr) +paul_spec_arr = np.abs(paul_spec_arr) + +obj = af.CWT(num=84, radix2_exp=12, samplate=sr, + low_fre=note_to_hz('C1'), bin_per_octave=12, + wavelet_type=WaveletContinueType.BUMP, + scale_type=SpectralFilterBankScaleType.OCTAVE) +bump_spec_arr = obj.cwt(audio_arr) +bump_spec_arr = np.abs(bump_spec_arr) + + +# Show spectrogram plot +fig, ax = plt.subplots(nrows=5, figsize=(8, 10), sharex=True) +fill_wave(audio_arr, samplate=sr, axes=ax[0]) + +fill_spec(morlet_spec_arr, axes=ax[1], + x_coords=obj.x_coords(), + y_coords=obj.y_coords(), + y_axis='log', + title='CWT-Octave MORLET Spectrogram') + +fill_spec(morse_spec_arr, axes=ax[2], + x_coords=obj.x_coords(), + y_coords=obj.y_coords(), + y_axis='log', + title='CWT-Octave MORSE Spectrogram') + +fill_spec(paul_spec_arr, axes=ax[3], + x_coords=obj.x_coords(), + y_coords=obj.y_coords(), + y_axis='log', + title='CWT-Octave PAUL Spectrogram') + +fill_spec(bump_spec_arr, axes=ax[4], + x_coords=obj.x_coords(), + y_coords=obj.y_coords(), + x_axis='time', y_axis='log', + title='CWT-Octave BUMP Spectrogram') +plt.show() +``` + + + + +## Spectral Features + +```python +import numpy as np +import matplotlib.pyplot as plt +import audioflux as af +from audioflux.display import fill_wave, fill_plot, fill_spec +from audioflux.type import SpectralDataType, SpectralFilterBankScaleType + +# Get a 220Hz's audio file path +sample_path = af.utils.sample_path('demo_guitar') + +# Read audio data and sample rate +audio_arr, sr = af.read(sample_path) + +bft_obj = af.BFT(num=256, samplate=sr, radix2_exp=12, slide_length=1024, + data_type=SpectralDataType.MAG, + scale_type=SpectralFilterBankScaleType.LINEAR) +spec_arr = bft_obj.bft(audio_arr) +spec_arr = np.abs(spec_arr) + +# Create Spectral object and extract flatness feature +spectral_obj = af.Spectral(num=bft_obj.num, + fre_band_arr=bft_obj.get_fre_band_arr()) +n_time = bft_obj.cal_time_length(len(audio_arr)) +spectral_obj.set_time_length(n_time) + +flatness_arr = spectral_obj.flatness(spec_arr) + +novelty_arr = spectral_obj.novelty(spec_arr) + +entropy_arr = spectral_obj.entropy(spec_arr) + +rms_arr = spectral_obj.rms(spec_arr) + +slope_arr = spectral_obj.slope(spec_arr) + +times = np.arange(0, len(flatness_arr)) * (bft_obj.slide_length / bft_obj.samplate) + +fig, ax = plt.subplots(nrows=7, sharex=True) +fill_wave(audio_arr, samplate=sr, axes=ax[0]) + +fill_spec(spec_arr, axes=ax[1], + x_coords=bft_obj.x_coords(len(audio_arr)), + y_coords=bft_obj.y_coords(), + y_axis='log', + title='Linear Mag Spectrogram') + +fill_plot(times, flatness_arr, axes=ax[2], label='flatness') + +fill_plot(times, novelty_arr, axes=ax[3], label='novelty') + +fill_plot(times, entropy_arr, axes=ax[4], label='entropy') + +fill_plot(times, rms_arr, axes=ax[5], label='rms_arr') + +fill_plot(times, slope_arr, axes=ax[6], label='slope_arr') + +plt.show() +``` + + + + + +## Pitch Estimate + +```python +import numpy as np +import audioflux as af +from audioflux import Pitch +import matplotlib.pyplot as plt +from audioflux.display import fill_wave +from audioflux.type import PitchType + +audio_arr, sr = af.read(af.utils.sample_path('demo_voice')) + +obj = Pitch(pitch_type=PitchType.YIN) + +fre_arr, value_arr1, value_arr2 = obj.pitch(audio_arr) +fre_arr[fre_arr<1] = np.nan + +times = np.arange(0, len(fre_arr)) * (obj.slide_length / obj.samplate) + +fig, ax = plt.subplots(nrows=2, sharex=True) +fill_wave(audio_arr, samplate=sr, axes=ax[0]) +ax[1].plot(times, fre_arr, label='fre', linewidth=3) +plt.show() +``` + + + +## Onset Detection + +```python +import numpy as np +import audioflux as af +import matplotlib.pyplot as plt +from audioflux.display import fill_wave, fill_plot, fill_spec +from audioflux.type import SpectralFilterBankScaleType, SpectralDataType, NoveltyType + +audio_arr, sr = af.read(af.utils.sample_path('demo_guitar')) + +bft_obj = af.BFT(num=128, samplate=sr, radix2_exp=11, slide_length=2048, + scale_type=SpectralFilterBankScaleType.MEL, + data_type=SpectralDataType.POWER) +spec_arr = bft_obj.bft(audio_arr) +spec_dB_arr = af.utils.power_to_db(np.abs(spec_arr)) + +n_fre, n_time = spec_dB_arr.shape +onset_obj = af.Onset(time_length=n_time, fre_length=n_fre, + slide_length=bft_obj.slide_length, samplate=bft_obj.samplate, + novelty_type=NoveltyType.FLUX) +point_arr, evn_arr, time_arr, value_arr = onset_obj.onset(spec_dB_arr) + + +audio_len = audio_arr.shape[0] +fig, axes = plt.subplots(nrows=3, sharex=True) +img = fill_spec(spec_dB_arr, axes=axes[0], + x_coords=bft_obj.x_coords(audio_len), + y_coords=bft_obj.y_coords(), + x_axis='time', y_axis='log', + title='Onset') + +ax = fill_wave(audio_arr, samplate=sr, axes=axes[1]) +ax.vlines(time_arr, -1, 1, color='r', alpha=0.9, + linestyle='--', label='Onsets') + +times = np.arange(0, len(evn_arr)) * (bft_obj.slide_length / sr) +ax = fill_plot(times, evn_arr, axes=axes[2], label='Onset strength') +ax.vlines(time_arr, evn_arr.min(), evn_arr.max(), color='r', alpha=0.9, + linestyle='--', label='Onsets') + +plt.show() +``` + + + + +### Harmonic-percussive source separation + +```python +import numpy as np +import audioflux as af +import matplotlib.pyplot as plt +from audioflux.display import fill_wave, fill_spec +from audioflux.type import SpectralDataType, WindowType, SpectralFilterBankScaleType + +audio_arr, sr = af.read(af.utils.sample_path('demo_guitar3')) + +radix2_exp = 11 +slide_length = (1 << radix2_exp) // 4 +hpss_obj = af.HPSS(radix2_exp=radix2_exp, window_type=WindowType.HAMM, + slide_length=slide_length, h_order=21, p_order=31) +h_arr, p_arr = hpss_obj.hpss(audio_arr) + +bft_obj = af.BFT(num=2049, radix2_exp=12, samplate=sr, + data_type=SpectralDataType.POWER, + scale_type=SpectralFilterBankScaleType.LINEAR) + +audio_arr = audio_arr[:len(h_arr)] + +origin_spec_arr = bft_obj.bft(audio_arr, result_type=1) +h_spec_arr = bft_obj.bft(h_arr, result_type=1) +p_spec_arr = bft_obj.bft(p_arr, result_type=1) + +origin_spec_arr = af.utils.power_to_abs_db(origin_spec_arr) +h_spec_arr = af.utils.power_to_abs_db(h_spec_arr) +p_spec_arr = af.utils.power_to_abs_db(p_spec_arr) + +audio_len = audio_arr.shape[0] + +fig, axes = plt.subplots(nrows=3, sharex=True, sharey=True) +fill_wave(audio_arr, samplate=sr, axes=axes[0]) +fill_wave(h_arr, samplate=sr, axes=axes[1]) +fill_wave(p_arr, samplate=sr, axes=axes[2]) + +fig, axes = plt.subplots(nrows=3, sharex=True, sharey=True) +fill_spec(origin_spec_arr, axes=axes[0], + x_coords=bft_obj.x_coords(audio_len), + y_coords=bft_obj.y_coords(), + y_axis='log', + title='origin spec') +fill_spec(h_spec_arr, axes=axes[1], + x_coords=bft_obj.x_coords(audio_len), + y_coords=bft_obj.y_coords(), + y_axis='log', + title='h spec') +fill_spec(p_spec_arr, axes=axes[2], + x_coords=bft_obj.x_coords(audio_len), + y_coords=bft_obj.y_coords(), + y_axis='log', + title='p spec') + +plt.show() +``` + + + \ No newline at end of file diff --git a/docs/installing.md b/docs/installing.md new file mode 100644 index 0000000..2418557 --- /dev/null +++ b/docs/installing.md @@ -0,0 +1,46 @@ + +## Compiling from source + +#### Linux build + +1. Installing dependencies on Linux + + For Ubuntu: + + ``` + sudo apt install libfftw3-dev + ``` + + For CentOS: + + ``` + sudo yum install -y fftw3-devel + ``` + +2. Python setup: + + ``` + $ python setup.py build + $ python setup.py install + ``` + +#### macOS build + +1. Installing dependencies on macOS + + Install Command Line Tools for Xcode. Even if you install Xcode from the app store you must configure command-line compilation by running: + + ``` + xcode-select --install + ``` + +2. Python setup: + + ``` + $ python setup.py build + $ python setup.py install + ``` + +#### Windows build + +Building from source is currently not supported. Only supports pip installation. 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062354833aeb21f5c3c2382d82261f22e059b2ce Mon Sep 17 00:00:00 2001 From: Liwei Dong Date: Thu, 19 Jan 2023 08:01:10 +0800 Subject: [PATCH 2/5] README update. --- README.md | 8 ++++++-- docs/examples.md | 17 ++++++++++++++++- 2 files changed, 22 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index a66c15a..2e237e1 100644 --- a/README.md +++ b/README.md @@ -47,7 +47,7 @@ Can be used for deep learning, pattern recognition, signal processing, bioinform - [Quickstart](#quickstart) - [Mel & MFCC](#mel--mfcc) - [CWT & Synchrosqueezing](#cwt--synchrosqueezing) - - [More examples](#more-examples) + - [Other examples](#other-examples) - [Installation](#installation) - [Python Package Intsall](#python-package-intsall) - [iOS build](#ios-build) @@ -135,7 +135,9 @@ The mir module contains the following algorithms: ## Quickstart -### Mel & Mfcc +### Mel & MFCC + +Mel spectrogram and Mel-frequency cepstral coefficients ```python # Feature extraction example @@ -184,6 +186,8 @@ plt.show() ### CWT & Synchrosqueezing +Continuous Wavelet Transform spectrogram and synchrosqueezing reassignment spectrogram + ```python # Feature extraction example import numpy as np diff --git a/docs/examples.md b/docs/examples.md index 45867d4..9fd5d4b 100644 --- a/docs/examples.md +++ b/docs/examples.md @@ -4,7 +4,7 @@ - [CQT & Chroma](#cqt--chroma) - [Wavelet Type](#wavelet-type) -- [Spectral Features](#spectral-feature) +- [Spectral Features](#spectral-features) - [Pitch Estimate](#pitch-estimate) - [Onset Detection](#onset-detection) - [Harmonic-percussive source separation](#harmonic-percussive-source-separation) @@ -12,6 +12,8 @@ ## CQT & Chroma +Constant-Q Transform spectrogram and cqt_chroma + ```python import numpy as np import audioflux as af @@ -60,6 +62,8 @@ plt.show() ## Wavelet Type +Comparison of different Wavelet Continue Type spectrogram + ```python import audioflux as af import numpy as np @@ -140,6 +144,8 @@ plt.show() ## Spectral Features +Flatness, Novelty, Entropy, RMS and Slope features + ```python import numpy as np import matplotlib.pyplot as plt @@ -205,6 +211,9 @@ plt.show() ## Pitch Estimate +Fundamental frequency (F0) estimation using the YIN algorithm. + + ```python import numpy as np import audioflux as af @@ -232,6 +241,9 @@ plt.show() ## Onset Detection +Locate note onset events by picking peaks in an onset strength envelope. + + ```python import numpy as np import audioflux as af @@ -279,6 +291,9 @@ plt.show() ### Harmonic-percussive source separation +Decompose audio into harmonic and percussive components + + ```python import numpy as np import audioflux as af From 4e949871a4a7f428b7cce117444d71256bb4d4cf Mon Sep 17 00:00:00 2001 From: Liwei Dong Date: Thu, 19 Jan 2023 08:02:18 +0800 Subject: [PATCH 3/5] README update. --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 2e237e1..1bb0a0e 100644 --- a/README.md +++ b/README.md @@ -245,7 +245,7 @@ plt.show() - [CQT & Chroma](docs/examples.md#cqt--chroma) - [Wavelet Type](docs/examples.md#wavelet-type) -- [Spectral Features](docs/examples.md#spectral-feature) +- [Spectral Features](docs/examples.md#spectral-features) - [Pitch Estimate](docs/examples.md#pitch-estimate) - [Onset Detection](docs/examples.md#onset-detection) - [Harmonic-percussive source separation](docs/examples.md#harmonic-percussive-source-separation) From 9fc2895efdff8e792d7b2b0514573f77035e248a Mon Sep 17 00:00:00 2001 From: weitianqi Date: Thu, 19 Jan 2023 08:14:47 +0800 Subject: [PATCH 4/5] fix python and fix docs --- audioflux/__version__.py | 2 +- audioflux/bft.py | 21 +- audioflux/core.py | 687 ++++++++- audioflux/cqt.py | 17 +- audioflux/cwt.py | 23 +- audioflux/display/__init__.py | 2 + audioflux/display/display.py | 297 ++++ audioflux/display/plot.py | 234 +++ audioflux/dsp/__init__.py | 2 + audioflux/dsp/czt.py | 90 ++ audioflux/dsp/resample.py | 200 +++ audioflux/dwt.py | 25 +- audioflux/feature/__init__.py | 4 + audioflux/feature/deconv.py | 130 ++ audioflux/feature/extractor.py | 471 ++++++ audioflux/feature/spectral.py | 2321 +++++++++++++++++++++++++++++ audioflux/feature/xxcc.py | 204 +++ audioflux/fst.py | 16 - audioflux/mir/harmonic_ratio.py | 4 +- audioflux/mir/hpss.py | 19 +- audioflux/mir/onset.py | 4 +- audioflux/mir/pitch.py | 19 +- audioflux/nsgt.py | 25 +- audioflux/pwt.py | 23 +- audioflux/reassign.py | 2 - audioflux/spectrogram.py | 13 +- audioflux/st.py | 16 - audioflux/swt.py | 32 +- audioflux/synsq.py | 4 +- audioflux/temporal.py | 15 + audioflux/type/basic.py | 20 +- audioflux/wpt.py | 26 +- audioflux/wsst.py | 6 +- docs/benchmark.rst | 17 +- docs/comparison.rst | 21 +- docs/image/comparisonBFTScale.png | Bin 61404 -> 40658 bytes docs/image/comparisonBFTStyle.png | Bin 60845 -> 38345 bytes docs/image/comparisonCQT_NSGT.png | Bin 51214 -> 31146 bytes docs/image/comparisonCWT_PWT.png | Bin 205787 -> 204981 bytes docs/image/comparisonChroma.png | Bin 53344 -> 39796 bytes docs/image/comparisonMFCC.png | Bin 81164 -> 84331 bytes docs/image/comparisonXXCC.png | Bin 69892 -> 75238 bytes docs/installation.rst | 73 +- docs/quickStart/cqt_chroma.rst | 8 +- docs/quickStart/mel.rst | 9 +- docs/quickStart/mfcc.rst | 8 +- docs/transforms/cqt.rst | 20 +- docs/transforms/cqtBase.rst | 8 + docs/transforms/simpleCqt.rst | 16 + docs/transforms/synsq.rst | 2 +- docs/transforms/transforms.rst | 2 +- setup.py | 12 +- 52 files changed, 4779 insertions(+), 391 deletions(-) create mode 100644 audioflux/display/__init__.py create mode 100644 audioflux/display/display.py create mode 100644 audioflux/display/plot.py create mode 100644 audioflux/dsp/__init__.py create mode 100644 audioflux/dsp/czt.py create mode 100644 audioflux/dsp/resample.py create mode 100644 audioflux/feature/__init__.py create mode 100644 audioflux/feature/deconv.py create mode 100644 audioflux/feature/extractor.py create mode 100644 audioflux/feature/spectral.py create mode 100644 audioflux/feature/xxcc.py create mode 100644 docs/transforms/cqtBase.rst create mode 100644 docs/transforms/simpleCqt.rst diff --git a/audioflux/__version__.py b/audioflux/__version__.py index 6318daa..28340d6 100644 --- a/audioflux/__version__.py +++ b/audioflux/__version__.py @@ -1,3 +1,3 @@ __title__ = 'audioflux' __description__ = 'A library for audio and music analysis, feature extraction.' -__version__ = '0.0.1' +__version__ = '0.1.1' diff --git a/audioflux/bft.py b/audioflux/bft.py index e0ff4fc..aeb001d 100644 --- a/audioflux/bft.py +++ b/audioflux/bft.py @@ -104,8 +104,6 @@ class BFT(Base): >>> import audioflux as af >>> audio_path = af.utils.sample_path('220') >>> audio_arr, sr = af.read(audio_path) - array([-5.5879354e-09, -9.3132257e-09, 0.0000000e+00, ..., - 3.2826858e-03, 3.2447521e-03, 3.0795704e-03], dtype=float32) Create BFT object of Linser(STFT) @@ -124,19 +122,6 @@ class BFT(Base): >>> spec_arr = obj.bft(audio_arr) >>> spec_arr = np.abs(spec_arr) >>> spec_dB_arr = power_to_db(spec_arr) - array([[-41.382824, -37.95072 , -50.98091 , ..., -48.275932, -66.01512 , - -53.229565], - [-29.873356, -33.225224, -32.94691 , ..., -49.855965, -49.439796, - -53.827766], - [-27.326801, -36.17459 , -32.978054, ..., -56.360283, -51.485504, - -51.036415], - ..., - [-80. , -80. , -80. , ..., -80. , -80. , - -80. ], - [-80. , -80. , -80. , ..., -80. , -80. , - -80. ], - [-80. , -80. , -80. , ..., -80. , -80. , - -80. ]], dtype=float32) Show spectrogram plot @@ -164,6 +149,12 @@ def __init__(self, num, radix2_exp=12, samplate=32000, is_reassign=False, is_temporal=False): super(BFT, self).__init__(pointer(OpaqueBFT())) + self.fft_length = fft_length = 1 << radix2_exp + + # check num + if num > (fft_length // 2 + 1): + raise ValueError(f'num={num} is too large') + # check BPO if scale_type == SpectralFilterBankScaleType.OCTAVE and bin_per_octave < 1: raise ValueError(f'bin_per_octave={bin_per_octave} must be a positive integer') diff --git a/audioflux/core.py b/audioflux/core.py index 3f15808..b372edc 100644 --- a/audioflux/core.py +++ b/audioflux/core.py @@ -18,7 +18,7 @@ def linear_spectrogram(X, num=None, radix2_exp=12, samplate=32000, slide_length= low_fre=0., window_type=WindowType.HANN, style_type=SpectralFilterBankStyleType.SLANEY, data_type=SpectralDataType.POWER, - is_reassign=False, result_type=0): + is_reassign=False): """ Short-time Fourier transform (Linear/STFT) @@ -26,7 +26,7 @@ def linear_spectrogram(X, num=None, radix2_exp=12, samplate=32000, slide_length= the sinusoidal frequency and phase content of local sections of a signal as it changes over time. - .. Note:: If you want to set more parameters, use the `BFT` class + .. Note:: We recommend using the `BFT` class, you can use it more flexibly and efficiently. Parameters ---------- @@ -59,27 +59,32 @@ def linear_spectrogram(X, num=None, radix2_exp=12, samplate=32000, slide_length= window_type: WindowType Window type for each frame. + See: `type.WindowType` + style_type: SpectralFilterBankStyleType Spectral filter bank style type. It determines the bank type of window. + see: `type.SpectralFilterBankStyleType` + data_type: SpectralDataType Spectrogram data type. It cat be set to mag or power. If you needs `db` type, you can set `power` type and then call the `power_to_db` method. + See: `type.SpectralDataType` + is_reassign: bool Whether to use reassign. - result_type: int,0 or 1 - - If `0`, then the result is a matrix of complex numbers. - - If `1`, then the result is a matrix of real numbers. - Returns ------- - out: np.ndarray [shape=(fre, time)] + spectrogram: np.ndarray [shape=(fre, time)] The matrix of Linear(STFT) + fre_band_arr: np:ndarray [shape=(fre,)] + The array of frequency bands + See Also -------- mel_spectrogram @@ -90,15 +95,50 @@ def linear_spectrogram(X, num=None, radix2_exp=12, samplate=32000, slide_length= NSGT CWT PWT + + Examples + -------- + + Read 220Hz audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('220') + >>> audio_arr, sr = af.read(audio_path) + + Extract spectrogram of dB + + >>> low_fre = 0 + >>> spec_arr, fre_band_arr = af.linear_spectrogram(audio_arr, samplate=sr, low_fre=low_fre) + >>> spec_dB_arr = af.utils.power_to_db(spec_arr) + + Show spectrogram plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_spec + >>> import numpy as np + >>> + >>> # calculate x/y-coords + >>> audio_len = audio_arr.shape[0] + >>> x_coords = np.linspace(0, audio_len/sr, spec_arr.shape[1] + 1) + >>> y_coords = np.insert(fre_band_arr, 0, low_fre) + >>> + >>> fig, ax = plt.subplots() + >>> img = fill_spec(spec_dB_arr, axes=ax, + >>> x_coords=x_coords, + >>> y_coords=y_coords, + >>> x_axis='time', y_axis='log', + >>> title='Linear Spectrogram') + >>> fig.colorbar(img, ax=ax, format="%+2.0f dB") """ if num is None: - num = (1 << radix2_exp) / 2 + 1 + num = (1 << radix2_exp) // 2 + 1 bft_obj = BFT(num=num, radix2_exp=radix2_exp, samplate=samplate, low_fre=low_fre, window_type=window_type, slide_length=slide_length, scale_type=SpectralFilterBankScaleType.LINEAR, style_type=style_type, data_type=data_type, is_reassign=is_reassign) - spec_arr = bft_obj.bft(X, result_type=result_type) - return spec_arr + spec_arr = bft_obj.bft(X, result_type=1) + fre_band_arr = bft_obj.get_fre_band_arr() + return spec_arr, fre_band_arr def mel_spectrogram(X, num=128, radix2_exp=12, samplate=32000, @@ -107,11 +147,11 @@ def mel_spectrogram(X, num=128, radix2_exp=12, samplate=32000, style_type=SpectralFilterBankStyleType.SLANEY, normal_type=SpectralFilterBankNormalType.NONE, data_type=SpectralDataType.POWER, - is_reassign=False, result_type=0): + is_reassign=False): """ Mel-scale spectrogram. - .. Note:: If you want to set more parameters, use the `BFT` class + .. Note:: We recommend using the `BFT` class, you can use it more flexibly and efficiently. Parameters ---------- @@ -136,6 +176,8 @@ def mel_spectrogram(X, num=128, radix2_exp=12, samplate=32000, window_type: WindowType Window type for each frame. + See: `type.WindowType` + slide_length: int or None Window sliding length. @@ -144,27 +186,32 @@ def mel_spectrogram(X, num=128, radix2_exp=12, samplate=32000, style_type: SpectralFilterBankStyleType Spectral filter bank style type. It determines the bank type of window. + see: `type.SpectralFilterBankStyleType` + normal_type: SpectralFilterBankNormalType Spectral filter normal type. It determines the type of normalization. + See: `type.SpectralFilterBankNormalType` + data_type: SpectralDataType Spectrogram data type. It cat be set to mag or power. If you needs `db` type, you can set `power` type and then call the `power_to_db` method. + See: `type.SpectralDataType` + is_reassign: bool Whether to use reassign. - result_type: int,0 or 1 - - If `0`, then the result is a matrix of complex numbers. - - If `1`, then the result is a matrix of real numbers. - Returns ------- - out: np.ndarray [shape=(fre, time)] + spectrogram: np.ndarray [shape=(fre, time)] The matrix of MEL + fre_band_arr: np:ndarray [shape=(fre,)] + The array of frequency bands + See Also -------- linear_spectrogram @@ -175,6 +222,41 @@ def mel_spectrogram(X, num=128, radix2_exp=12, samplate=32000, NSGT CWT PWT + + Examples + -------- + + Read 220Hz audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('220') + >>> audio_arr, sr = af.read(audio_path) + + Extract spectrogram of dB + + >>> low_fre = 0 + >>> spec_arr, fre_band_arr = af.mel_spectrogram(audio_arr, samplate=sr, low_fre=low_fre) + >>> spec_dB_arr = af.utils.power_to_db(spec_arr) + + Show spectrogram plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_spec + >>> import numpy as np + >>> + >>> # calculate x/y-coords + >>> audio_len = audio_arr.shape[0] + >>> x_coords = np.linspace(0, audio_len/sr, spec_arr.shape[1] + 1) + >>> y_coords = np.insert(fre_band_arr, 0, low_fre) + >>> + >>> fig, ax = plt.subplots() + >>> img = fill_spec(spec_dB_arr, axes=ax, + >>> x_coords=x_coords, + >>> y_coords=y_coords, + >>> x_axis='time', y_axis='log', + >>> title='Mel Spectrogram') + >>> fig.colorbar(img, ax=ax, format="%+2.0f dB") + """ bft_obj = BFT(num=num, radix2_exp=radix2_exp, samplate=samplate, low_fre=low_fre, high_fre=high_fre, @@ -182,8 +264,9 @@ def mel_spectrogram(X, num=128, radix2_exp=12, samplate=32000, scale_type=SpectralFilterBankScaleType.MEL, style_type=style_type, normal_type=normal_type, data_type=data_type, is_reassign=is_reassign) - spec_arr = bft_obj.bft(X, result_type=result_type) - return spec_arr + spec_arr = bft_obj.bft(X, result_type=1) + fre_band_arr = bft_obj.get_fre_band_arr() + return spec_arr, fre_band_arr def bark_spectrogram(X, num=128, radix2_exp=12, samplate=32000, @@ -192,11 +275,11 @@ def bark_spectrogram(X, num=128, radix2_exp=12, samplate=32000, style_type=SpectralFilterBankStyleType.SLANEY, normal_type=SpectralFilterBankNormalType.NONE, data_type=SpectralDataType.POWER, - is_reassign=False, result_type=0): + is_reassign=False): """ Bark-scale spectrogram. - .. Note:: If you want to set more parameters, use the `BFT` class + .. Note:: We recommend using the `BFT` class, you can use it more flexibly and efficiently. Parameters ---------- @@ -221,6 +304,8 @@ def bark_spectrogram(X, num=128, radix2_exp=12, samplate=32000, window_type: WindowType Window type for each frame. + See: `type.WindowType` + slide_length: int or None Window sliding length. @@ -229,27 +314,32 @@ def bark_spectrogram(X, num=128, radix2_exp=12, samplate=32000, style_type: SpectralFilterBankStyleType Spectral filter bank style type. It determines the bank type of window. + see: `type.SpectralFilterBankStyleType` + normal_type: SpectralFilterBankNormalType Spectral filter normal type. It determines the type of normalization. + See: `type.SpectralFilterBankNormalType` + data_type: SpectralDataType Spectrogram data type. It cat be set to mag or power. If you needs `db` type, you can set `power` type and then call the `power_to_db` method. + See: `type.SpectralDataType` + is_reassign: bool Whether to use reassign. - result_type: int,0 or 1 - - If `0`, then the result is a matrix of complex numbers. - - If `1`, then the result is a matrix of real numbers. - Returns ------- - out: np.ndarray [shape=(fre, time)] + spectrogram: np.ndarray [shape=(fre, time)] The matrix of BARK + fre_band_arr: np:ndarray [shape=(fre,)] + The array of frequency bands + See Also -------- linear_spectrogram @@ -260,6 +350,40 @@ def bark_spectrogram(X, num=128, radix2_exp=12, samplate=32000, NSGT CWT PWT + + Examples + -------- + + Read 220Hz audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('220') + >>> audio_arr, sr = af.read(audio_path) + + Extract spectrogram of dB + + >>> low_fre = 0 + >>> spec_arr, fre_band_arr = af.bark_spectrogram(audio_arr, samplate=sr, low_fre=low_fre) + >>> spec_dB_arr = af.utils.power_to_db(spec_arr) + + Show spectrogram plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_spec + >>> import numpy as np + >>> + >>> # calculate x/y-coords + >>> audio_len = audio_arr.shape[0] + >>> x_coords = np.linspace(0, audio_len/sr, spec_arr.shape[1] + 1) + >>> y_coords = np.insert(fre_band_arr, 0, low_fre) + >>> + >>> fig, ax = plt.subplots() + >>> img = fill_spec(spec_dB_arr, axes=ax, + >>> x_coords=x_coords, + >>> y_coords=y_coords, + >>> x_axis='time', y_axis='log', + >>> title='Bark Spectrogram') + >>> fig.colorbar(img, ax=ax, format="%+2.0f dB") """ bft_obj = BFT(num=num, radix2_exp=radix2_exp, samplate=samplate, low_fre=low_fre, high_fre=high_fre, @@ -267,8 +391,9 @@ def bark_spectrogram(X, num=128, radix2_exp=12, samplate=32000, scale_type=SpectralFilterBankScaleType.BARK, style_type=style_type, normal_type=normal_type, data_type=data_type, is_reassign=is_reassign) - spec_arr = bft_obj.bft(X, result_type=result_type) - return spec_arr + spec_arr = bft_obj.bft(X, result_type=1) + fre_band_arr = bft_obj.get_fre_band_arr() + return spec_arr, fre_band_arr def erb_spectrogram(X, num=128, radix2_exp=12, samplate=32000, @@ -277,11 +402,11 @@ def erb_spectrogram(X, num=128, radix2_exp=12, samplate=32000, style_type=SpectralFilterBankStyleType.SLANEY, normal_type=SpectralFilterBankNormalType.NONE, data_type=SpectralDataType.POWER, - is_reassign=False, result_type=0): + is_reassign=False): """ Erb-scale spectrogram. - .. Note:: If you want to set more parameters, use the `BFT` class + .. Note:: We recommend using the `BFT` class, you can use it more flexibly and efficiently. Parameters ---------- @@ -306,6 +431,8 @@ def erb_spectrogram(X, num=128, radix2_exp=12, samplate=32000, window_type: WindowType Window type for each frame. + See: `type.WindowType` + slide_length: int or None Window sliding length. @@ -314,27 +441,32 @@ def erb_spectrogram(X, num=128, radix2_exp=12, samplate=32000, style_type: SpectralFilterBankStyleType Spectral filter bank style type. It determines the bank type of window. + see: `type.SpectralFilterBankStyleType` + normal_type: SpectralFilterBankNormalType Spectral filter normal type. It determines the type of normalization. + See: `type.SpectralFilterBankNormalType` + data_type: SpectralDataType Spectrogram data type. It cat be set to mag or power. If you needs `db` type, you can set `power` type and then call the `power_to_db` method. + See: `type.SpectralDataType` + is_reassign: bool Whether to use reassign. - result_type: int,0 or 1 - - If `0`, then the result is a matrix of complex numbers. - - If `1`, then the result is a matrix of real numbers. - Returns ------- out: np.ndarray [shape=(fre, time)] The matrix of ERB + fre_band_arr: np:ndarray [shape=(fre,)] + The array of frequency bands + See Also -------- linear_spectrogram @@ -345,6 +477,40 @@ def erb_spectrogram(X, num=128, radix2_exp=12, samplate=32000, NSGT CWT PWT + + Examples + -------- + + Read 220Hz audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('220') + >>> audio_arr, sr = af.read(audio_path) + + Extract spectrogram of dB + + >>> low_fre = 0 + >>> spec_arr, fre_band_arr = af.erb_spectrogram(audio_arr, samplate=sr, low_fre=low_fre) + >>> spec_dB_arr = af.utils.power_to_db(spec_arr) + + Show spectrogram plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_spec + >>> import numpy as np + >>> + >>> # calculate x/y-coords + >>> audio_len = audio_arr.shape[0] + >>> x_coords = np.linspace(0, audio_len/sr, spec_arr.shape[1] + 1) + >>> y_coords = np.insert(fre_band_arr, 0, low_fre) + >>> + >>> fig, ax = plt.subplots() + >>> img = fill_spec(spec_dB_arr, axes=ax, + >>> x_coords=x_coords, + >>> y_coords=y_coords, + >>> x_axis='time', y_axis='log', + >>> title='Erb Spectrogram') + >>> fig.colorbar(img, ax=ax, format="%+2.0f dB") """ bft_obj = BFT(num=num, radix2_exp=radix2_exp, samplate=samplate, low_fre=low_fre, high_fre=high_fre, @@ -352,16 +518,19 @@ def erb_spectrogram(X, num=128, radix2_exp=12, samplate=32000, scale_type=SpectralFilterBankScaleType.ERB, style_type=style_type, normal_type=normal_type, data_type=data_type, is_reassign=is_reassign) - spec_arr = bft_obj.bft(X, result_type=result_type) - return spec_arr + spec_arr = bft_obj.bft(X, result_type=1) + fre_band_arr = bft_obj.get_fre_band_arr() + return spec_arr, fre_band_arr def mfcc(X, cc_num=13, rectify_type=CepstralRectifyType.LOG, - num=128, radix2_exp=12, samplate=32000, slide_length=None, + mel_num=128, radix2_exp=12, samplate=32000, slide_length=None, low_fre=None, high_fre=None, window_type=WindowType.HANN): """ Mel-frequency cepstral coefficients (MFCCs) + .. Note:: We recommend using the `BFT` and `XXCC` class, you can use it more flexibly and efficiently. + Parameters ---------- X: np.ndarray [shape=(n,)] @@ -373,7 +542,7 @@ def mfcc(X, cc_num=13, rectify_type=CepstralRectifyType.LOG, rectify_type: CepstralRectifyType cepstral rectify type - num: int + mel_num: int Number of mel frequency bins to generate, starting at `low_fre`. radix2_exp: int @@ -396,11 +565,16 @@ def mfcc(X, cc_num=13, rectify_type=CepstralRectifyType.LOG, window_type: WindowType Window type for each frame. + See: `type.WindowType` + Returns ------- out: np.ndarray [shape=(cc_num, time)] The matrix of MFCCs + fre_band_arr: np:ndarray [shape=(fre,)] + The array of Mel frequency bands + See Also -------- bfcc @@ -409,8 +583,37 @@ def mfcc(X, cc_num=13, rectify_type=CepstralRectifyType.LOG, BFT XXCC + + Examples + -------- + + Read 220Hz audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('220') + >>> audio_arr, sr = af.read(audio_path) + + Extract mfcc data + + >>> cc_arr, _ = af.mfcc(audio_arr, samplate=sr) + + Show plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_spec + >>> import numpy as np + >>> + >>> # calculate x-coords + >>> audio_len = audio_arr.shape[0] + >>> x_coords = np.linspace(0, audio_len/sr, cc_arr.shape[1] + 1) + >>> + >>> fig, ax = plt.subplots() + >>> img = fill_spec(cc_arr, axes=ax, + >>> x_coords=x_coords, x_axis='time', + >>> title='MFCC') + >>> fig.colorbar(img, ax=ax) """ - bft_obj = BFT(num=num, radix2_exp=radix2_exp, samplate=samplate, + bft_obj = BFT(num=mel_num, radix2_exp=radix2_exp, samplate=samplate, low_fre=low_fre, high_fre=high_fre, window_type=window_type, slide_length=slide_length, scale_type=SpectralFilterBankScaleType.MEL, @@ -422,15 +625,18 @@ def mfcc(X, cc_num=13, rectify_type=CepstralRectifyType.LOG, xxcc_obj = XXCC(num=bft_obj.num) xxcc_obj.set_time_length(time_length=spec_arr.shape[1]) xxcc_arr = xxcc_obj.xxcc(spec_arr, cc_num=cc_num, rectify_type=rectify_type) - return xxcc_arr + fre_band_arr = bft_obj.get_fre_band_arr() + return xxcc_arr, fre_band_arr def bfcc(X, cc_num=13, rectify_type=CepstralRectifyType.LOG, - num=128, radix2_exp=12, samplate=32000, slide_length=None, + bark_num=128, radix2_exp=12, samplate=32000, slide_length=None, low_fre=None, high_fre=None, window_type=WindowType.HANN): """ Bark-frequency cepstral coefficients (BFCCs) + .. Note:: We recommend using the `BFT` and `XXCC` class, you can use it more flexibly and efficiently. + Parameters ---------- X: np.ndarray [shape=(n,)] @@ -442,8 +648,8 @@ def bfcc(X, cc_num=13, rectify_type=CepstralRectifyType.LOG, rectify_type: CepstralRectifyType cepstral rectify type - num: int - Number of mel frequency bins to generate, starting at `low_fre`. + bark_num: int + Number of bark frequency bins to generate, starting at `low_fre`. radix2_exp: int ``fft_length=2**radix2_exp`` @@ -465,11 +671,16 @@ def bfcc(X, cc_num=13, rectify_type=CepstralRectifyType.LOG, window_type: WindowType Window type for each frame. + See: `type.WindowType` + Returns ------- out: np.ndarray [shape=(cc_num, time)] The matrix of BFCCs + fre_band_arr: np:ndarray [shape=(fre,)] + The array of Bark frequency bands + See Also -------- mfcc @@ -478,8 +689,37 @@ def bfcc(X, cc_num=13, rectify_type=CepstralRectifyType.LOG, BFT XXCC + + Examples + -------- + + Read 220Hz audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('220') + >>> audio_arr, sr = af.read(audio_path) + + Extract bfcc data + + >>> cc_arr, _ = af.bfcc(audio_arr, samplate=sr) + + Show plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_spec + >>> import numpy as np + >>> + >>> # calculate x-coords + >>> audio_len = audio_arr.shape[0] + >>> x_coords = np.linspace(0, audio_len/sr, cc_arr.shape[1] + 1) + >>> + >>> fig, ax = plt.subplots() + >>> img = fill_spec(cc_arr, axes=ax, + >>> x_coords=x_coords, x_axis='time', + >>> title='BFCC') + >>> fig.colorbar(img, ax=ax) """ - bft_obj = BFT(num=num, radix2_exp=radix2_exp, samplate=samplate, + bft_obj = BFT(num=bark_num, radix2_exp=radix2_exp, samplate=samplate, low_fre=low_fre, high_fre=high_fre, window_type=window_type, slide_length=slide_length, scale_type=SpectralFilterBankScaleType.BARK, @@ -491,15 +731,18 @@ def bfcc(X, cc_num=13, rectify_type=CepstralRectifyType.LOG, xxcc_obj = XXCC(num=bft_obj.num) xxcc_obj.set_time_length(time_length=spec_arr.shape[1]) xxcc_arr = xxcc_obj.xxcc(spec_arr, cc_num=cc_num, rectify_type=rectify_type) - return xxcc_arr + fre_band_arr = bft_obj.get_fre_band_arr() + return xxcc_arr, fre_band_arr def gtcc(X, cc_num=13, rectify_type=CepstralRectifyType.LOG, - num=128, radix2_exp=12, samplate=32000, slide_length=None, + erb_num=128, radix2_exp=12, samplate=32000, slide_length=None, low_fre=None, high_fre=None, window_type=WindowType.HANN): """ Gammatone cepstral coefficients (GTCCs) + .. Note:: We recommend using the `BFT` and `XXCC` class, you can use it more flexibly and efficiently. + Parameters ---------- X: np.ndarray [shape=(n,)] @@ -511,8 +754,8 @@ def gtcc(X, cc_num=13, rectify_type=CepstralRectifyType.LOG, rectify_type: CepstralRectifyType cepstral rectify type - num: int - Number of mel frequency bins to generate, starting at `low_fre`. + erb_num: int + Number of erb frequency bins to generate, starting at `low_fre`. radix2_exp: int ``fft_length=2**radix2_exp`` @@ -534,11 +777,16 @@ def gtcc(X, cc_num=13, rectify_type=CepstralRectifyType.LOG, window_type: WindowType Window type for each frame. + See: `type.WindowType` + Returns ------- out: np.ndarray [shape=(cc_num, time)] The matrix of GTCCs + fre_band_arr: np:ndarray [shape=(fre,)] + The array of Erb frequency bands + See Also -------- mfcc @@ -547,8 +795,37 @@ def gtcc(X, cc_num=13, rectify_type=CepstralRectifyType.LOG, BFT XXCC + + Examples + -------- + + Read 220Hz audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('220') + >>> audio_arr, sr = af.read(audio_path) + + Extract gtcc data + + >>> cc_arr, _ = af.gtcc(audio_arr, samplate=sr) + + Show plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_spec + >>> import numpy as np + >>> + >>> # calculate x-coords + >>> audio_len = audio_arr.shape[0] + >>> x_coords = np.linspace(0, audio_len/sr, cc_arr.shape[1] + 1) + >>> + >>> fig, ax = plt.subplots() + >>> img = fill_spec(cc_arr, axes=ax, + >>> x_coords=x_coords, x_axis='time', + >>> title='GTCC') + >>> fig.colorbar(img, ax=ax) """ - bft_obj = BFT(num=num, radix2_exp=radix2_exp, samplate=samplate, + bft_obj = BFT(num=erb_num, radix2_exp=radix2_exp, samplate=samplate, low_fre=low_fre, high_fre=high_fre, window_type=window_type, slide_length=slide_length, scale_type=SpectralFilterBankScaleType.ERB, @@ -556,22 +833,24 @@ def gtcc(X, cc_num=13, rectify_type=CepstralRectifyType.LOG, normal_type=SpectralFilterBankNormalType.AREA, data_type=SpectralDataType.POWER, is_reassign=False) spec_arr = bft_obj.bft(X) - print(spec_arr.shape) xxcc_obj = XXCC(num=bft_obj.num) xxcc_obj.set_time_length(time_length=spec_arr.shape[1]) xxcc_arr = xxcc_obj.xxcc(spec_arr, cc_num=cc_num, rectify_type=rectify_type) - return xxcc_arr + fre_band_arr = bft_obj.get_fre_band_arr() + return xxcc_arr, fre_band_arr def cqcc(X, cc_num=13, rectify_type=CepstralRectifyType.LOG, - num=84, samplate=32000, low_fre=note_to_hz('C1'), slide_length=None, + cqt_num=84, samplate=32000, low_fre=note_to_hz('C1'), slide_length=None, bin_per_octave=12, window_type=WindowType.HANN, normal_type=SpectralFilterBankNormalType.AREA, is_scale=True): """ Constant-Q cepstral coefficients (CQCCs) + .. Note:: We recommend using the :ref:`CQT ` class, you can use it more flexibly and efficiently. + Parameters ---------- X: np.ndarray [shape=(n,)] @@ -583,8 +862,8 @@ def cqcc(X, cc_num=13, rectify_type=CepstralRectifyType.LOG, rectify_type: CepstralRectifyType cepstral rectify type - num: int - Number of mel frequency bins to generate, starting at `low_fre`. + cqt_num: int + Number of cqt frequency bins to generate, starting at `low_fre`. samplate: int Sampling rate of the incoming audio. @@ -603,16 +882,23 @@ def cqcc(X, cc_num=13, rectify_type=CepstralRectifyType.LOG, window_type: WindowType Window type for each frame. + See: `type.WindowType` + normal_type: SpectralFilterBankNormalType Spectral filter normal type. It determines the type of normalization. + See: `type.SpectralFilterBankNormalType` + is_scale: bool Whether to use scale. Returns ------- out: np.ndarray [shape=(cc_num, time)] - The matrix of GTCCs + The matrix of CQCCs + + fre_band_arr: np:ndarray [shape=(fre,)] + The array of cqt frequency bands See Also -------- @@ -621,15 +907,44 @@ def cqcc(X, cc_num=13, rectify_type=CepstralRectifyType.LOG, gtcc CQT + + Examples + -------- + + Read 220Hz audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('220') + >>> audio_arr, sr = af.read(audio_path) + + Extract cqcc data + + >>> cc_arr, _ = af.cqcc(audio_arr, samplate=sr) + + Show plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_spec + >>> import numpy as np + >>> + >>> # calculate x-coords + >>> audio_len = audio_arr.shape[0] + >>> x_coords = np.linspace(0, audio_len/sr, cc_arr.shape[1] + 1) + >>> + >>> fig, ax = plt.subplots() + >>> img = fill_spec(cc_arr, axes=ax, + >>> x_coords=x_coords, x_axis='time', + >>> title='CQCC') + >>> fig.colorbar(img, ax=ax) """ - cqt_obj = CQT(num=num, samplate=samplate, low_fre=low_fre, slide_length=slide_length, + cqt_obj = CQT(num=cqt_num, samplate=samplate, low_fre=low_fre, slide_length=slide_length, bin_per_octave=bin_per_octave, window_type=window_type, normal_type=normal_type, is_scale=is_scale) spec_arr = cqt_obj.cqt(X) - print(spec_arr.shape) power_arr = np.abs(spec_arr) ** 2 cqcc_arr = cqt_obj.cqcc(power_arr, cc_num=cc_num, rectify_type=rectify_type) - return cqcc_arr + fre_band_arr = cqt_obj.get_fre_band_arr() + return cqcc_arr, fre_band_arr def cqt(X, num=84, samplate=32000, low_fre=note_to_hz('C1'), bin_per_octave=12, @@ -640,6 +955,8 @@ def cqt(X, num=84, samplate=32000, low_fre=note_to_hz('C1'), bin_per_octave=12, """ Constant-Q transform (CQT) + .. Note:: We recommend using the :ref:`CQT ` class, you can use it more flexibly and efficiently. + Parameters ---------- X: np.ndarray [shape=(n,)] @@ -668,12 +985,16 @@ def cqt(X, num=84, samplate=32000, low_fre=note_to_hz('C1'), bin_per_octave=12, window_type: WindowType Window type for each frame. + See: `type.WindowType` + slide_length: int Window sliding length. normal_type: SpectralFilterBankNormalType Spectral filter normal type. It determines the type of normalization. + See: `type.SpectralFilterBankNormalType` + is_scale: bool Whether to use scale. @@ -681,13 +1002,59 @@ def cqt(X, num=84, samplate=32000, low_fre=note_to_hz('C1'), bin_per_octave=12, ------- out: np.ndarray [shape=(num, time)] The matrix of CQT + + fre_band_arr: np:ndarray [shape=(fre,)] + The array of frequency bands + + See Also + -------- + cqcc + vqt + + CQT + + Examples + -------- + + Read 220Hz audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('220') + >>> audio_arr, sr = af.read(audio_path) + + Extract spectrogram of dB + + >>> low_fre = af.utils.note_to_hz('C1') + >>> spec_arr, fre_band_arr = af.cqt(audio_arr, samplate=sr, low_fre=low_fre) + >>> spec_dB_arr = af.utils.power_to_db(spec_arr ** 2) + + Show spectrogram plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_spec + >>> import numpy as np + >>> + >>> # calculate x/y-coords + >>> audio_len = audio_arr.shape[0] + >>> x_coords = np.linspace(0, audio_len/sr, spec_arr.shape[1] + 1) + >>> y_coords = np.insert(fre_band_arr, 0, low_fre) + >>> + >>> fig, ax = plt.subplots() + >>> img = fill_spec(spec_dB_arr, axes=ax, + >>> x_coords=x_coords, + >>> y_coords=y_coords, + >>> x_axis='time', y_axis='log', + >>> title='CQT') + >>> fig.colorbar(img, ax=ax, format="%+2.0f dB") """ cqt_obj = CQT(num=num, samplate=samplate, low_fre=low_fre, slide_length=slide_length, bin_per_octave=bin_per_octave, window_type=window_type, normal_type=normal_type, is_scale=is_scale, factor=factor, beta=0., thresh=thresh) cqt_arr = cqt_obj.cqt(X) - return cqt_arr + cqt_arr = np.abs(cqt_arr) + fre_band_arr = cqt_obj.get_fre_band_arr() + return cqt_arr, fre_band_arr def vqt(X, num=84, samplate=32000, low_fre=note_to_hz('C1'), bin_per_octave=12, @@ -698,6 +1065,8 @@ def vqt(X, num=84, samplate=32000, low_fre=note_to_hz('C1'), bin_per_octave=12, """ Variable-Q transform (VQT) + .. Note:: We recommend using the :ref:`CQT ` class, you can use it more flexibly and efficiently. + Parameters ---------- X: np.ndarray [shape=(n,)] @@ -729,30 +1098,79 @@ def vqt(X, num=84, samplate=32000, low_fre=note_to_hz('C1'), bin_per_octave=12, window_type: WindowType Window type for each frame. + See: `type.WindowType` + slide_length: int Window sliding length. normal_type: SpectralFilterBankNormalType Spectral filter normal type. It determines the type of normalization. + See: `type.SpectralFilterBankNormalType` + is_scale: bool Whether to use scale. Returns ------- out: np.ndarray [shape=(num, time)] - The matrix of CQT + The matrix of VQT + + fre_band_arr: np:ndarray [shape=(fre,)] + The array of frequency bands + + See Also + -------- + cqcc + vqt + + CQT + + Examples + -------- + + Read 220Hz audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('220') + >>> audio_arr, sr = af.read(audio_path) + + Extract spectrogram of dB + >>> low_fre = af.utils.note_to_hz('C1') + >>> spec_arr, fre_band_arr = af.vqt(audio_arr, samplate=sr, low_fre=low_fre) + >>> spec_dB_arr = af.utils.power_to_db(spec_arr ** 2) + + Show spectrogram plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_spec + >>> import numpy as np + >>> + >>> # calculate x/y-coords + >>> audio_len = audio_arr.shape[0] + >>> x_coords = np.linspace(0, audio_len/sr, spec_arr.shape[1] + 1) + >>> y_coords = np.insert(fre_band_arr, 0, low_fre) + >>> + >>> fig, ax = plt.subplots() + >>> img = fill_spec(spec_dB_arr, axes=ax, + >>> x_coords=x_coords, + >>> y_coords=y_coords, + >>> x_axis='time', y_axis='log', + >>> title='VQT') + >>> fig.colorbar(img, ax=ax, format="%+2.0f dB") """ - cqt_obj = CQT(num=num, samplate=samplate, low_fre=low_fre, slide_length=slide_length, + vqt_obj = CQT(num=num, samplate=samplate, low_fre=low_fre, slide_length=slide_length, bin_per_octave=bin_per_octave, window_type=window_type, normal_type=normal_type, is_scale=is_scale, factor=factor, beta=beta, thresh=thresh) - cqt_arr = cqt_obj.cqt(X) - return cqt_arr + vqt_arr = vqt_obj.cqt(X) + vqt_arr = np.abs(vqt_arr) + fre_band_arr = vqt_obj.get_fre_band_arr() + return vqt_arr, fre_band_arr def chroma_linear(X, chroma_num=12, radix2_exp=12, samplate=32000, low_fre=0., high_fre=16000., - window_type=WindowType.HANN, slide_length=1024, + window_type=WindowType.HANN, slide_length=1024, data_type=SpectralDataType.POWER, style_type=SpectralFilterBankStyleType.SLANEY, normal_type=SpectralFilterBankNormalType.NONE): """ @@ -773,7 +1191,7 @@ def chroma_linear(X, chroma_num=12, radix2_exp=12, samplate=32000, low_fre=0., h Sampling rate of the incoming audio. low_fre: float - Lowest frequency. `default: 32.703(C1)` + Lowest frequency. high_fre: float or None Highest frequency. Default is `16000(samplate/2)`. @@ -781,15 +1199,26 @@ def chroma_linear(X, chroma_num=12, radix2_exp=12, samplate=32000, low_fre=0., h window_type: WindowType Window type for each frame. + See: `type.WindowType` + slide_length: int Window sliding length. + data_type: SpectralDataType + Spectrogram data type. + + See: `type.SpectralDataType` + style_type: SpectralFilterBankStyleType Spectral filter bank style type. It determines the bank type of window. + see: `type.SpectralFilterBankStyleType` + normal_type: SpectralFilterBankNormalType Spectral filter normal type. It determines the type of normalization. + See: `type.SpectralFilterBankNormalType` + Returns ------- out: np.ndarray [shape=(chroma_num, time)] @@ -799,20 +1228,51 @@ def chroma_linear(X, chroma_num=12, radix2_exp=12, samplate=32000, low_fre=0., h -------- chroma_cqt chroma_octave + + Examples + -------- + + Read 220Hz audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('220') + >>> audio_arr, sr = af.read(audio_path) + + Extract chroma_linear data + + >>> chroma_arr = af.chroma_linear(audio_arr, samplate=sr) + + Show spectrogram plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_spec + >>> import numpy as np + >>> + >>> # calculate x-coords + >>> audio_len = audio_arr.shape[0] + >>> x_coords = np.linspace(0, audio_len/sr, chroma_arr.shape[1] + 1) + >>> + >>> fig, ax = plt.subplots() + >>> img = fill_spec(chroma_arr, axes=ax, + >>> x_coords=x_coords, + >>> x_axis='time', y_axis='chroma', + >>> title='Chroma_linear') + >>> fig.colorbar(img, ax=ax) """ spec_obj = Spectrogram(num=chroma_num, radix2_exp=radix2_exp, samplate=samplate, low_fre=low_fre, high_fre=high_fre, window_type=window_type, slide_length=slide_length, filter_bank_type=SpectralFilterBankType.CHROMA, - filter_style_type=style_type, filter_normal_type=normal_type) + filter_style_type=style_type, filter_normal_type=normal_type, + data_type=data_type) spec_arr = spec_obj.spectrogram(X) return spec_arr -def chroma_octave(X, chroma_num=12, radix2_exp=12, samplate=32000, low_fre=0., high_fre=16000., - window_type=WindowType.HANN, slide_length=1024, - style_type=SpectralFilterBankStyleType.SLANEY, normal_type=SpectralFilterBankNormalType.NONE): +def chroma_octave(X, chroma_num=12, radix2_exp=12, samplate=32000, low_fre=note_to_hz('C1'), high_fre=16000., + window_type=WindowType.HANN, slide_length=1024, data_type=SpectralDataType.POWER, + style_type=SpectralFilterBankStyleType.SLANEY, normal_type=SpectralFilterBankNormalType.NONE): """ Octave chromagram @@ -839,15 +1299,26 @@ def chroma_octave(X, chroma_num=12, radix2_exp=12, samplate=32000, low_fre=0., h window_type: WindowType Window type for each frame. + See: `type.WindowType` + slide_length: int Window sliding length. + data_type: SpectralDataType + Spectrogram data type. + + See: `type.SpectralDataType` + style_type: SpectralFilterBankStyleType Spectral filter bank style type. It determines the bank type of window. + see: `type.SpectralFilterBankStyleType` + normal_type: SpectralFilterBankNormalType Spectral filter normal type. It determines the type of normalization. + See: `type.SpectralFilterBankNormalType` + Returns ------- out: np.ndarray [shape=(chroma_num, time)] @@ -857,10 +1328,40 @@ def chroma_octave(X, chroma_num=12, radix2_exp=12, samplate=32000, low_fre=0., h -------- chroma_linear chroma_cqt + + Examples + -------- + + Read 220Hz audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('220') + >>> audio_arr, sr = af.read(audio_path) + + Extract chroma_octave data + + >>> chroma_arr = af.chroma_octave(audio_arr, samplate=sr) + + Show spectrogram plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_spec + >>> import numpy as np + >>> + >>> # calculate x-coords + >>> audio_len = audio_arr.shape[0] + >>> x_coords = np.linspace(0, audio_len/sr, chroma_arr.shape[1] + 1) + >>> + >>> fig, ax = plt.subplots() + >>> img = fill_spec(chroma_arr, axes=ax, + >>> x_coords=x_coords, + >>> x_axis='time', y_axis='chroma', + >>> title='Chroma_octave') + >>> fig.colorbar(img, ax=ax) """ spec_obj = Spectrogram(num=chroma_num, radix2_exp=radix2_exp, samplate=samplate, low_fre=low_fre, high_fre=high_fre, window_type=window_type, - slide_length=slide_length, + slide_length=slide_length, data_type=data_type, filter_bank_type=SpectralFilterBankType.OCTAVE_CHROMA, filter_style_type=style_type, filter_normal_type=normal_type) @@ -869,13 +1370,15 @@ def chroma_octave(X, chroma_num=12, radix2_exp=12, samplate=32000, low_fre=0., h def chroma_cqt(X, chroma_num=12, num=84, samplate=32000, low_fre=note_to_hz('C1'), bin_per_octave=12, - factor=1., beta=0., thresh=0.01, + factor=1., thresh=0.01, window_type=WindowType.HANN, slide_length=None, normal_type=SpectralFilterBankNormalType.AREA, is_scale=True): """ Constant-Q chromagram + .. Note:: We recommend using the :ref:`CQT ` class, you can use it more flexibly and efficiently. + Parameters ---------- X: np.ndarray [shape=(n,)] @@ -901,21 +1404,22 @@ def chroma_cqt(X, chroma_num=12, num=84, samplate=32000, low_fre=note_to_hz('C1' factor: float Factor value - beta: float - Beta value - thresh: float Thresh value window_type: WindowType Window type for each frame. + See: `type.WindowType` + slide_length: int Window sliding length. normal_type: SpectralFilterBankNormalType Spectral filter normal type. It determines the type of normalization. + See: `type.SpectralFilterBankNormalType` + is_scale: bool Whether to use scale. @@ -928,11 +1432,42 @@ def chroma_cqt(X, chroma_num=12, num=84, samplate=32000, low_fre=note_to_hz('C1' -------- chroma_linear chroma_octave + + Examples + -------- + + Read 220Hz audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('220') + >>> audio_arr, sr = af.read(audio_path) + + Extract chroma_cqt data + + >>> chroma_arr = af.chroma_cqt(audio_arr, samplate=sr) + + Show spectrogram plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_spec + >>> import numpy as np + >>> + >>> # calculate x-coords + >>> audio_len = audio_arr.shape[0] + >>> x_coords = np.linspace(0, audio_len/sr, chroma_arr.shape[1] + 1) + >>> + >>> fig, ax = plt.subplots() + >>> img = fill_spec(chroma_arr, axes=ax, + >>> x_coords=x_coords, + >>> x_axis='time', y_axis='chroma', + >>> title='Chroma_cqt') + >>> fig.colorbar(img, ax=ax) """ cqt_obj = CQT(num=num, samplate=samplate, low_fre=low_fre, slide_length=slide_length, bin_per_octave=bin_per_octave, window_type=window_type, normal_type=normal_type, is_scale=is_scale, - factor=factor, beta=beta, thresh=thresh) + factor=factor, beta=0, thresh=thresh) cqt_arr = cqt_obj.cqt(X) - chroma_arr = cqt_obj.chroma(cqt_arr, chroma_num=chroma_num) + power_arr = cqt_arr ** 2 + chroma_arr = cqt_obj.chroma(power_arr, chroma_num=chroma_num, data_type=SpectralDataType.POWER) return chroma_arr diff --git a/audioflux/cqt.py b/audioflux/cqt.py index c4672fa..3bddb57 100644 --- a/audioflux/cqt.py +++ b/audioflux/cqt.py @@ -156,6 +156,8 @@ def chroma(self, m_cqt_data, chroma_num=12, data_type: SpectralDataType Data type of CQT spectrogram matrix + See: `type.SpectralDataType` + norm_type: ChromaDataNormalType Normalization type of chroma @@ -539,8 +541,6 @@ class CQT(CQTBase): >>> import audioflux as af >>> audio_path = af.utils.sample_path('220') >>> audio_arr, sr = af.read(audio_path) - array([-5.5879354e-09, -9.3132257e-09, 0.0000000e+00, ..., - 3.2826858e-03, 3.2447521e-03, 3.0795704e-03], dtype=float32) Create CQT object @@ -554,19 +554,6 @@ class CQT(CQTBase): >>> import numpy as np >>> spec_arr = obj.cqt(audio_arr) >>> spec_mag_arr = np.abs(spec_arr) - array([[4.13306177e-01, 4.24238801e-01, 4.03985798e-01, ..., - 9.09681246e-03, 7.72366347e-03, 6.51519699e-03], - [3.45392436e-01, 3.47385347e-01, 3.18555593e-01, ..., - 3.74186062e-03, 3.57810827e-03, 5.09629818e-03], - [2.74609476e-01, 2.69073159e-01, 2.38167673e-01, ..., - 1.73897278e-02, 1.37754036e-02, 1.05445199e-02], - ..., - [6.24795386e-04, 4.99437414e-02, 1.60291921e-02, ..., - 1.42244404e-04, 1.27240157e-04, 7.22698495e-03], - [8.64346686e-04, 3.18217799e-02, 2.55288873e-02, ..., - 1.96875044e-04, 1.22444399e-04, 7.08540343e-03], - [5.32156206e-04, 3.65380235e-02, 1.94845423e-02, ..., - 1.01396305e-04, 4.90328348e-05, 6.83017401e-03]], dtype=float32) Show CQT spectrogram plot diff --git a/audioflux/cwt.py b/audioflux/cwt.py index ea03033..5c51a36 100644 --- a/audioflux/cwt.py +++ b/audioflux/cwt.py @@ -96,8 +96,6 @@ class CWT(Base): >>> # CWT can only input fft_length data >>> # For radix2_exp=12, then fft_length=4096 >>> audio_arr = audio_arr[:4096] - array([-5.5879354e-09, -9.3132257e-09, 0.0000000e+00, ..., - -1.3137090e-01, -1.5649168e-01, -1.8550715e-01], dtype=float32) Create CWT object of Octave @@ -113,19 +111,6 @@ class CWT(Base): >>> import numpy as np >>> spec_arr = obj.cwt(audio_arr) >>> spec_arr = np.abs(spec_arr) - array([[5.9040589e-03, 5.9040883e-03, 5.9041460e-03, ..., 3.3131682e-03, - 3.3131670e-03, 3.3131654e-03], - [1.1005097e-02, 1.1005105e-02, 1.1005125e-02, ..., 5.0106235e-03, - 5.0106221e-03, 5.0106202e-03], - [1.4031209e-02, 1.4031209e-02, 1.4031206e-02, ..., 5.7992758e-03, - 5.7992786e-03, 5.7992795e-03], - ..., - [2.9626326e-08, 2.8529593e-08, 2.7170261e-08, ..., 3.9147399e-03, - 4.0606451e-03, 4.1351509e-03], - [8.1756388e-09, 7.8093771e-09, 6.8754065e-09, ..., 4.8332815e-03, - 5.0402004e-03, 5.1454846e-03], - [1.8423437e-08, 1.8048882e-08, 2.0733696e-08, ..., 7.7667395e-03, - 7.9389615e-03, 8.0277789e-03]], dtype=float32) Show spectrogram plot @@ -147,6 +132,12 @@ def __init__(self, num, radix2_exp=12, samplate=32000, gamma=None, beta=None, is_padding=True): super(CWT, self).__init__(pointer(OpaqueCWT())) + self.fft_length = fft_length = 1 << radix2_exp + + # check num + if num > (fft_length // 2 + 1): + raise ValueError(f'num={num} is too large') + # check BPO if scale_type == SpectralFilterBankScaleType.OCTAVE and bin_per_octave < 1: raise ValueError(f'bin_per_octave={bin_per_octave} must be a positive integer') @@ -178,8 +169,6 @@ def __init__(self, num, radix2_exp=12, samplate=32000, if beta is None: beta = default_beta - self.fft_length = 1 << radix2_exp - self.num = num self.radix2_exp = radix2_exp self.samplate = samplate diff --git a/audioflux/display/__init__.py b/audioflux/display/__init__.py new file mode 100644 index 0000000..bc493e1 --- /dev/null +++ b/audioflux/display/__init__.py @@ -0,0 +1,2 @@ +from .plot import Plot +from .display import fill_spec, fill_plot, fill_wave diff --git a/audioflux/display/display.py b/audioflux/display/display.py new file mode 100644 index 0000000..1074218 --- /dev/null +++ b/audioflux/display/display.py @@ -0,0 +1,297 @@ +import warnings + +import numpy as np +import matplotlib.pyplot as plt +import matplotlib.axes as plaxes +from matplotlib.cm import get_cmap +from matplotlib.ticker import Formatter, ScalarFormatter, MaxNLocator, SymmetricalLogLocator, FixedLocator +from audioflux.utils import midi_to_note + + +def _axis_scale(axes, ax_name, ax_type): + """Set the x/y-axis scale.""" + + if ax_name == "x": + scaler = axes.set_xscale + else: + scaler = axes.set_yscale + + kwargs = {} + if ax_type == "log": + mode = "symlog" + kwargs['base'] = 2 + kwargs['linthresh'] = 64 + kwargs['linscale'] = 0.5 + else: + mode = "linear" + scaler(mode, **kwargs) + + +def _axes_check(axes): + """Check if "axes" is an instance of an axis object. If not, use `subplot`.""" + if axes is None: + axes = plt.subplot() + elif not isinstance(axes, plaxes.Axes): + raise ValueError("`axes` must be an instance of `matplotlib.axes.Axes`") + return axes + + +def _axis_decorate(axis, ax_type, coords): + """Configure axis tickers, formatters, locators, and labels""" + + if ax_type is None: + if len(coords) <= 2: + axis.set_ticks(coords) + axis.set_label_text("") + + elif ax_type == "time": + axis.set_major_formatter(TimeFormatter(unit=None, lag=False)) + axis.set_major_locator(MaxNLocator(prune=None, steps=[1, 1.5, 5, 6, 10])) + axis.set_label_text("Time") + + elif ax_type == "log": + axis.set_major_formatter(ScalarFormatter()) + axis.set_major_locator(SymmetricalLogLocator(axis.get_transform())) + + elif ax_type == "linear": + axis.set_major_formatter(ScalarFormatter()) + + elif ax_type == "chroma": + coords_len = len(coords) + if (coords_len - 1) % 12 != 0: + raise ValueError(f'The number={coords_len - 1} of y-axis scales of chroma must be a multiple of 12') + bin_per_tone = (coords_len - 1) // 12 + axis.set_major_formatter(ChromaFormatter(bin_per_tone=bin_per_tone)) + + degrees = np.array([0, 2, 4, 5, 7, 9, 11]) + axis.set_major_locator(FixedLocator(degrees * bin_per_tone)) + axis.set_label_text("Pitch class") + else: + raise ValueError("Unsupported axis type: {}".format(ax_type)) + + +def _same_axes(x_axis, y_axis, xlim, ylim): + """Check if two axes are the same, used to determine squared plots""" + axes_same_and_not_none = (x_axis == y_axis) and (x_axis is not None) + axes_same_lim = xlim == ylim + return axes_same_and_not_none and axes_same_lim + + +class TimeFormatter(Formatter): + + def __init__(self, lag=False, unit=None): + + if unit not in ["s", "ms", None]: + raise ValueError("Unknown time unit: {}".format(unit)) + + self.unit = unit + self.lag = lag + + def __call__(self, x, pos=None): + """Return the time format as pos""" + + _, dmax = self.axis.get_data_interval() + vmin, vmax = self.axis.get_view_interval() + + # In lag-time axes, anything greater than dmax / 2 is negative time + if self.lag and x >= dmax * 0.5: + # In lag mode, don't tick past the limits of the data + if x > dmax: + return "" + value = np.abs(x - dmax) + # Do we need to tweak vmin/vmax here? + sign = "-" + else: + value = x + sign = "" + + if self.unit == "s": + s = "{:.3g}".format(value) + elif self.unit == "ms": + s = "{:.3g}".format(value * 1000) + else: + if vmax - vmin > 3600: + # Hours viz + s = "{:d}:{:02d}:{:02d}".format( + int(value / 3600.0), + int(np.mod(value / 60.0, 60)), + int(np.mod(value, 60)), + ) + elif vmax - vmin > 60: + # Minutes viz + s = "{:d}:{:02d}".format(int(value / 60.0), int(np.mod(value, 60))) + elif vmax - vmin >= 1: + # Seconds viz + s = "{:.2g}".format(value) + else: + # Milliseconds viz + s = "{:.3f}".format(value) + + return "{:s}{:s}".format(sign, s) + + +class ChromaFormatter(Formatter): + + def __init__(self, bin_per_tone=1): + self.bin_per_tone = bin_per_tone + + def __call__(self, x, pos=None): + x = int(x) // self.bin_per_tone + r = midi_to_note(x, is_octave=False) + return r + + +def fill_spec( + data, + axes=None, + x_coords=None, + y_coords=None, + x_axis=None, + y_axis=None, + title='' +): + """ + Display a spectrogram data + + Parameters + ---------- + data: np.ndarray [shape=(fre, time)] + The matrix of spectrogram + + axes: matplotlib.axes.Axes or None + Axes to plot on instead of the default `plt.subplot()`. + + x_coords, y_coords: None or np.ndarray [shape=(n,)] + Array of X-axis/Y-axis ticks + + If not provided, it will be divided equally according to the size of the matrix + + x_axis, y_axis: str or None + The scale of X-axis/Y-axis + + - None: Displayed on a linear scale. + - 'linear': Displayed on a linear scale. + - 'log': Displayed on a log scale. + - 'chroma': Pitches are determined by the chroma filters. + - 'time': Markers are shown as seconds. + + title: str + The title of subplot + + Returns + ------- + matplotlib.collections.QuadMesh + """ + if np.iscomplexobj(data): + warnings.warn('Display after performing abs on complex numbers') + data = np.abs(data) + + if x_coords is None: + x_coords = np.arange(data.shape[1] + 1) + if y_coords is None: + y_coords = np.arange(data.shape[0] + 1) + + if y_axis == 'chroma': + y_coords = np.arange(data.shape[0] + 1) + + cmap = get_cmap('plasma') + collection = axes.pcolormesh(x_coords, y_coords, data, cmap=cmap) + + axes.set_xlim(np.min(x_coords), np.max(x_coords)) + axes.set_ylim(np.min(y_coords), np.max(y_coords)) + + _axis_scale(axes, "x", x_axis) + _axis_scale(axes, "y", y_axis) + + _axis_decorate(axes.xaxis, x_axis, x_coords) + _axis_decorate(axes.yaxis, y_axis, y_coords) + + if title: + axes.set_title(title) + + return collection + + +def fill_plot(x, y, axes=None, label='', is_legend=True, *, + x_lims=None, y_lims=None, y_blank_threshold=0.15): + """ + Display a plot data + + Parameters + ---------- + x: np.ndarray [shape=(time,)] + The horizontal(time) coordinates of the data points. + + y: np.ndarray [shape=(data,)] + The vertical coordinates of the data points. + + axes: matplotlib.axes.Axes or None + Axes to plot on instead of the default `plt.subplot()`. + + label: str + Set a label for the Axes. + + x_lims: None or (left: float, right: float) + Set the x-axis view limits. + + * None: Automatically get the minimum and maximum values for the left and right of the x-axis coordinates. + * (left: float, right: float): The left and right of the x-axis coordinates. + + y_lims: None or (bottom: float, top: float) + Set the y-axis view limits. + + * None: Automatically get the minimum and maximum values for the bottom and top of the x-axis coordinates. + * (bottom: float, top: float): The bottom and top of the y-axis coordinates. + + y_blank_threshold: float + The space between the minimum and maximum values of the data and the y-axis scale. + + Returns + ------- + axes: matplotlib.axes.Axes + """ + axes = _axes_check(axes) + + if not x_lims: + x_lims = (np.min(x), np.max(x)) + x_lims = tuple(x_lims) + + if not y_lims: + y_min = np.min(y) + y_max = np.max(y) + y_blank_len = np.abs(y_max - y_min) * y_blank_threshold + y_lims = (y_min - y_blank_len, y_max + y_blank_len) + y_lims = tuple(y_lims) + + axes.set_xlim(*x_lims) + axes.set_ylim(*y_lims) + + axes.plot(x, y, label=label) + if is_legend: + axes.legend() + return axes + + +def fill_wave(data, samplate=32000, axes=None): + """ + Display a wave data + + Parameters + ---------- + data: np.ndarray [shape=(n,)] + Input audio data + + axes: matplotlib.axes.Axes or None + Axes to plot on instead of the default `plt.subplot()`. + + samplate: int + Sampling rate of the incoming audio + + Returns + ------- + axes: matplotlib.axes.Axes + """ + times = np.arange(data.shape[0]) / samplate + return fill_plot(times, data, axes=axes, + x_lims=(times[0], times[-1]), + is_legend=False, y_blank_threshold=0.15) diff --git a/audioflux/display/plot.py b/audioflux/display/plot.py new file mode 100644 index 0000000..efaeda9 --- /dev/null +++ b/audioflux/display/plot.py @@ -0,0 +1,234 @@ +import warnings +import matplotlib.pyplot as plt +import numpy as np + +from .display import fill_spec, fill_plot, fill_wave + +__all__ = ['Plot'] + + +class Plot(object): + """ + Create a set of subplots for audioflux. + + Parameters + ---------- + nrows: int, default: 1 + Number of rows of the subplot grid. + + ncols: int, default: 1 + Number of columns of the subplot grid. + + sharex bool or {'none', 'all', 'row', 'col'}, default: False + Controls sharing of properties among x (*sharex*) + + sharey: bool or {'none', 'all', 'row', 'col'}, default: False + Controls sharing of properties among y (*sharey*) + + fig_width: float + Width in inches. + + fig_height: float + Height in inches. + + fig_kw: + Finger params + + """ + + def __init__(self, nrows=1, ncols=1, sharex=False, sharey=False, fig_width=8, fig_height=8, fig_kw=None): + self.nrows = nrows + self.ncols = ncols + fig_kw = fig_kw or {} + fig_kw['figsize'] = (fig_width, fig_height) + self.fig, self.ax = plt.subplots(nrows, ncols, sharex=sharex, sharey=sharey, **fig_kw) + + def get_axes(self, row, col): + """ + Get axes + + Parameters + ---------- + row: int + Index of row of the subplot grid. + + col: int + Index of col of the subplot grid. + + Returns + ------- + ax: matplotlib.axes.Axes + """ + if self.nrows == 1 and self.ncols == 1: + return self.ax + elif self.nrows == 1: + return self.ax[col] + elif self.ncols == 1: + return self.ax[row] + return self.ax[row][col] + + def add_spec_data(self, data, x_coords=None, y_coords=None, scale='linear', row_idx=0, col_idx=0, title='', + *, show_colorbar=True, axis_option='on'): + """ + Add spectrogram data + + Parameters + ---------- + data: np.ndarray [shape=(fre, time)] + The matrix of spectrogram + + x_coords, y_coords: None or np.ndarray [shape=(n,)] + Array of X-axis/Y-axis ticks + + If not provided, it will be divided equally according to the size of the matrix + + scale: str + The scale of Y-axis + + - 'linear': the spectrum is displayed on a linear scale. + - 'log': the spectrum is displayed on a log scale. + + If ``y_coords`` is None, it will always be linear + + row_idx: + Index of row of the subplot grid. + + col_idx: + Index of col of the subplot grid. + + title: str + Set a title for the Axes. + + show_colorbar: bool + Whether to display the colorbar + + axis_option: bool or str + See `matplotlib._AxesBase.axis()` for details. + + Returns + ------- + ax: matplotlib.axes.Axes + """ + if x_coords is None: + x_axis = None + else: + x_axis = 'time' + if y_coords is None and scale != 'linear': + scale = 'linear' + warnings.warn(f'If `y_coords` is None, `scale` must be linear') + ax = self.get_axes(row_idx, col_idx) + img = fill_spec(data, ax, x_coords=x_coords, y_coords=y_coords, x_axis=x_axis, y_axis=scale, title=title) + if show_colorbar: + self.fig.colorbar(img, ax=ax) + plt.axis(axis_option) + return ax + + def add_wave_data(self, data, samplate=32000, row_idx=0, col_idx=0): + """ + Add wave data + + Parameters + ---------- + data: np.ndarray [shape=(n,)] + Input audio data + + samplate: int + Sampling rate of the incoming audio + + row_idx: + Index of row of the subplot grid + + col_idx: + Index of col of the subplot grid + + + Returns + ------- + ax: matplotlib.axes.Axes + """ + axes = self.get_axes(row=row_idx, col=col_idx) + return fill_wave(data, samplate=samplate, axes=axes) + + def add_plot(self, x, y, label='', row_idx=0, col_idx=0, is_legend=True, *, + x_lims=None, y_lims=None, y_blank_threshold=0.15): + """ + Add plot data + + Parameters + ---------- + x: np.ndarray [shape=(time,)] + The horizontal(time) coordinates of the data points. + + y: np.ndarray [shape=(data,)] + The vertical coordinates of the data points. + + label: str + Set a label for the Axes. + + row_idx, col_idx: int + Index of row/col of the subplot grid. + + x_lims: None or (left: float, right: float) + Set the x-axis view limits. + + * None: Automatically get the minimum and maximum values for the left and right of the x-axis coordinates. + * (left: float, right: float): The left and right of the x-axis coordinates. + + y_lims: None or (bottom: float, top: float) + Set the y-axis view limits. + + * None: Automatically get the minimum and maximum values for the bottom and top of the x-axis coordinates. + * (bottom: float, top: float): The bottom and top of the y-axis coordinates. + + y_blank_threshold: float + The space between the minimum and maximum values of the data and the y-axis scale. + + Returns + ------- + ax: matplotlib.axes.Axes + """ + axes = self.get_axes(row_idx, col_idx) + + axes = fill_plot(x, y, axes=axes, label=label, is_legend=is_legend, + x_lims=x_lims, y_lims=y_lims, y_blank_threshold=y_blank_threshold) + return axes + + def show(self): + """ + Show plot + """ + plt.show() + + def save(self, fname, *, dpi='figure', format=None, metadata=None, + bbox_inches=None, pad_inches=0.1, + facecolor='auto', edgecolor='auto', + backend=None, **kwargs + ): + """ + Save plot to file + + See `matplotlib.savefig` + """ + + plt.savefig(fname, dpi=dpi, format=format, metadata=metadata, + bbox_inches=bbox_inches, pad_inches=pad_inches, + facecolor=facecolor, edgecolor=edgecolor, + backend=backend, **kwargs + ) + + def close(self, fig='all'): + """ + Close a figure window. + + Parameters + ---------- + fig : None or int or str or `.Figure` + The figure to close. There are a number of ways to specify this: + + - *None*: the current figure + - `.Figure`: the given `.Figure` instance + - ``int``: a figure number + - ``str``: a figure name + - 'all': all figures + """ + plt.close(fig) diff --git a/audioflux/dsp/__init__.py b/audioflux/dsp/__init__.py new file mode 100644 index 0000000..0121f30 --- /dev/null +++ b/audioflux/dsp/__init__.py @@ -0,0 +1,2 @@ +from .czt import * +from .resample import * diff --git a/audioflux/dsp/czt.py b/audioflux/dsp/czt.py new file mode 100644 index 0000000..e437a4d --- /dev/null +++ b/audioflux/dsp/czt.py @@ -0,0 +1,90 @@ +import numpy as np +from ctypes import Structure, POINTER, pointer, c_int, c_float, c_void_p +from audioflux.base import Base +from audioflux.utils import check_audio + +__all__ = ["CZT"] + + +class OpaqueCZT(Structure): + _fields_ = [] + + +class CZT(Base): + """ + Chirp Z-transform (CZT) + + Parameters + ---------- + radix2_exp: int + ``fft_length = 2 ** radix2_exp`` + + Examples + -------- + >>> import numpy as np + >>> from audioflux import CZT + >>> n = np.arange(4096) + >>> c_obj = CZT(3) + >>> czt_arr = c_obj.czt(n, 0.15, 0.25) + >>> czt_arr = np.abs(czt_arr) + >>> czt_arr + array([7.4057508, 6.685393 , 6.4312935, ..., 0. , 0. , + 0. ], dtype=float32) + """ + + def __init__(self, radix2_exp): + super(CZT, self).__init__(pointer(OpaqueCZT())) + + self.radix2_exp = radix2_exp + + fn = self._lib['cztObj_new'] + fn.argtypes = [POINTER(POINTER(OpaqueCZT)), c_int] + fn(self._obj, c_int(self.radix2_exp)) + self._is_created = True + + def czt(self, data_arr, low_w, high_w): + """ + Compute the czt + + Parameters + ---------- + data_arr: np.ndarray [shape=(n,)] + Input audio 1D array + low_w: float + high_w: float + + Returns + ------- + out: np.ndarray [shape=(n_time)] + """ + data_arr = np.asarray(data_arr, dtype=np.float32, order='C') + check_audio(data_arr) + + czt_fn = self._lib['cztObj_czt'] + czt_fn.argtypes = [POINTER(OpaqueCZT), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS'), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS'), + c_float, c_float, + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS'), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS')] + + real_arr = data_arr.real + imag_arr = data_arr.imag + + data_length = data_arr.size + size = data_length * 2 + + ret_real_arr = np.zeros(size, dtype=np.float32) + ret_imag_arr = np.zeros(size, dtype=np.float32) + + czt_fn(self._obj, real_arr, imag_arr, + c_float(low_w), c_float(high_w), + ret_real_arr, ret_imag_arr) + return ret_real_arr + (ret_imag_arr * 1j) + + def __del__(self): + if self._is_created: + free_fn = self._lib['cztObj_free'] + free_fn.argtypes = [POINTER(OpaqueCZT)] + free_fn.restype = c_void_p + free_fn(self._obj) diff --git a/audioflux/dsp/resample.py b/audioflux/dsp/resample.py new file mode 100644 index 0000000..7f73de8 --- /dev/null +++ b/audioflux/dsp/resample.py @@ -0,0 +1,200 @@ +import numpy as np +from ctypes import Structure, POINTER, pointer, c_int, c_float, c_void_p +from audioflux.base import Base +from audioflux.type import ResampleQualityType, WindowType +from audioflux.utils import check_audio + +__all__ = ["Resample", "WindowResample"] + + +class OpaqueResample(Structure): + _fields_ = [] + + +class ResampleBase(Base): + def __init__(self): + super(ResampleBase, self).__init__(pointer(OpaqueResample())) + + def set_samplate(self, source_rate, target_rate): + """ + Set samplate + + Parameters + ---------- + source_rate: int + target_rate: int + """ + fn = self._lib['resampleObj_setSamplate'] + fn.argtypes = [POINTER(OpaqueResample), c_int, c_int] + fn(self._obj, c_int(source_rate), c_int(target_rate)) + + def enable_continue(self, flag): + """ + Enable continue + + Parameters + ---------- + flag: int + """ + fn = self._lib['resampleObj_enableContinue'] + fn.argtypes = [POINTER(OpaqueResample), c_int] + fn(self._obj, c_int(flag)) + + def cal_data_length(self, data_length): + """ + Compute the data length + + Parameters + ---------- + data_length: int + + Returns + ------- + out: int + """ + fn = self._lib['resampleObj_calDataLength'] + fn.argtypes = [POINTER(OpaqueResample), c_int] + fn.restype = c_int + fn(self._obj, c_int(data_length)) + + def debug(self): + fn = self._lib['resampleObj_debug'] + fn.argtypes = [POINTER(OpaqueResample)] + fn(self._obj) + + def resample(self, data_arr): + """ + Resample for audio data + + Parameters + ---------- + data_arr: np.ndarray [shape=(n,)] + Input audio array + + Returns + ------- + out: np.ndarray [shape=(n,)] + Audio data after resampling + """ + data_arr = np.asarray(data_arr, dtype=np.float32, order='C') + check_audio(data_arr) + + fn = self._lib['resampleObj_resample'] + fn.argtypes = [POINTER(OpaqueResample), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS'), + c_int, + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS')] + data_len = data_arr.shape[0] + + ret_arr = np.zeros(data_len * 5, dtype=np.float32) + + new_arr_len = fn(self._obj, data_arr, data_len, ret_arr) + return ret_arr[:new_arr_len] + + def __del__(self): + if self._is_created: + free_fn = self._lib['resampleObj_free'] + free_fn.argtypes = [POINTER(OpaqueResample)] + free_fn.restype = c_void_p + free_fn(self._obj) + + +class Resample(ResampleBase): + """ + Calculate resampling + + Parameters + ---------- + qual_type: ResampleQualityType + is_scale: bool + Whether to use scale + + Examples + -------- + >>> import audioflux as af + >>> from audioflux.type import ResampleQualityType + >>> audio_data, sr = af.read(af.utils.sample_path('220')) + >>> + >>> resample_obj = af.Resample(qual_type=ResampleQualityType.BEST, is_scale=False) + >>> new_audio_data = resample_obj.resample(audio_data) + >>> new_audio_data + array([ 7.7589704e-07, -1.4843295e-06, 2.4068654e-06, ..., + 2.6401449e-03, 3.1324192e-03, 3.3868181e-03], dtype=float32) + """ + + def __init__(self, qual_type=ResampleQualityType.BEST, is_scale=False): + super(Resample, self).__init__() + + self.qual_type = qual_type + self.is_scale = is_scale + self.is_continue = False + + fn = self._lib['resampleObj_new'] + fn.argtypes = [POINTER(POINTER(OpaqueResample)), + POINTER(c_int), + POINTER(c_int), + POINTER(c_int)] + fn(self._obj, + None if self.qual_type is None else pointer(c_int(self.qual_type.value)), + pointer(c_int(int(self.is_scale))), + pointer(c_int(int(self.is_continue)))) + self._is_created = True + + +class WindowResample(ResampleBase): + """ + Calculate window resample + + Parameters + ---------- + zero_num: int + nbit: int + win_type: WindowType + value: float + roll_off: float + is_scale: bool + Whether to use scale + + Examples + -------- + >>> import audioflux as af + >>> from audioflux.type import ResampleQualityType + >>> audio_data, sr = af.read(af.utils.sample_path('220')) + >>> + >>> resample_obj = af.WindowResample() + >>> new_audio_data = resample_obj.resample(audio_data) + >>> new_audio_data + array([ 1.0972841e-06, -2.0991588e-06, 3.4038217e-06, ..., + 3.7337288e-03, 4.4299099e-03, 4.7896840e-03], dtype=float32) + """ + + def __init__(self, zero_num=64, nbit=9, win_type=WindowType.HANN, + value=None, roll_off=0.945, is_scale=False): + super(WindowResample, self).__init__() + + self.zero_num = zero_num + self.nbit = nbit + self.win_type = win_type + self.value = value + self.roll_off = roll_off + self.is_scale = is_scale + self.is_continue = False + + fn = self._lib['resampleObj_new'] + fn.argtypes = [POINTER(POINTER(OpaqueResample)), + POINTER(c_int), + POINTER(c_int), + POINTER(c_int), + POINTER(c_float), + POINTER(c_float), + POINTER(c_int), + POINTER(c_int)] + fn(self._obj, + pointer(c_int(self.zero_num)), + pointer(c_int(self.nbit)), + pointer(c_int(self.win_type.value)), + None if self.value is None else pointer(c_float(self.value)), + pointer(c_float(self.roll_off)), + pointer(c_int(int(self.is_scale))), + pointer(c_int(int(self.is_continue)))) + self._is_created = True diff --git a/audioflux/dwt.py b/audioflux/dwt.py index 8ed312e..147d74d 100644 --- a/audioflux/dwt.py +++ b/audioflux/dwt.py @@ -74,9 +74,6 @@ class DWT(Base): >>> audio_path = af.utils.sample_path('880') >>> audio_arr, sr = af.read(audio_path) >>> audio_arr = audio_arr[:4096] - >>> audio_arr - array([-5.5879354e-09, -9.3132257e-09, 0.0000000e+00, ..., - -1.3013066e-01, -8.8289484e-02, -7.2360471e-02], dtype=float32) Create DWT object @@ -90,37 +87,19 @@ class DWT(Base): >>> import numpy as np >>> coef_arr, m_data_arr = obj.dwt(audio_arr) >>> m_data_arr = np.abs(m_data_arr) - >>> coef_arr - array([ 0.01794817, -0.11379103, 0.01852291, ..., 0.00575202, - -0.01306073, 0.01146322], dtype=float32) - - >>> m_data_arr - array([[ 0.13798241, 0.13798241, 0.13798241, ..., 0.06006188, - 0.06006188, 0.06006188], - [-0.03932592, -0.03932592, -0.03932592, ..., -0.00320929, - -0.00320929, -0.00320929], - [ 0.19744706, 0.19744706, 0.19744706, ..., 0.02507702, - 0.02507702, 0.02507702], - ..., - [-0.03494655, -0.03494655, -0.03494655, ..., 0.03548484, - 0.03548484, 0.03548484], - [-0.03214145, -0.03214145, -0.03214145, ..., 0.01973005, - 0.01973005, 0.01973005], - [-0.02876501, -0.02876501, 0.00548246, ..., -0.06147008, - -0.00574392, -0.00574392]], dtype=float32) Show plot >>> import matplotlib.pyplot as plt >>> from audioflux.display import fill_spec, fill_plot - >>> fig, ax = plt.subplots(nrows=2, sharex=True) + >>> fig, ax = plt.subplots(nrows=2) >>> fill_spec(m_data_arr, axes=ax[0], >>> x_coords=obj.x_coords(), >>> y_coords=obj.y_coords(), >>> x_axis='time', y_axis='log', >>> title='DWT') >>> - >>> fill_plot(obj.x_coords()[1:], coef_arr, + >>> fill_plot(np.arange(len(coef_arr)), coef_arr, >>> axes=ax[1], label='DWT-coef') """ diff --git a/audioflux/feature/__init__.py b/audioflux/feature/__init__.py new file mode 100644 index 0000000..8320507 --- /dev/null +++ b/audioflux/feature/__init__.py @@ -0,0 +1,4 @@ +from .deconv import * +from .spectral import * +from .xxcc import * +from .extractor import * diff --git a/audioflux/feature/deconv.py b/audioflux/feature/deconv.py new file mode 100644 index 0000000..9c9838c --- /dev/null +++ b/audioflux/feature/deconv.py @@ -0,0 +1,130 @@ +import numpy as np +from ctypes import Structure, POINTER, pointer, c_int, c_void_p +from audioflux.base import Base +from audioflux.utils import ascontiguous_T + +__all__ = ["Deconv"] + + +class OpaqueDeconv(Structure): + _fields_ = [] + + +class Deconv(Base): + """ + Deconvolution for spectrum, supports all spectrum types. + + Parameters + ---------- + num: int + Number of frequency bins to generate. It must be the same as the + `num` parameter of the transformation (same as the spectrogram matrix). + + Examples + -------- + + Get a 880Hz's audio file + + >>> import audioflux as af + >>> sample_path = af.utils.sample_path('880') + >>> audio_arr, sr = af.read(sample_path) + + Create BFT object and extract mel spectrogram + + >>> import numpy as np + >>> from audioflux.type import SpectralFilterBankScaleType, SpectralDataType + >>> bft_obj = af.BFT(num=128, radix2_exp=12, samplate=sr, + >>> scale_type=SpectralFilterBankScaleType.MEL, + >>> data_type=SpectralDataType.POWER) + >>> spec_arr = bft_obj.bft(audio_arr) + >>> spec_arr = np.abs(spec_arr) + + Create Deconv object and extract deconv + + >>> deconv_obj = af.Deconv(bft_obj.num) + >>> deconv_obj.set_time_length(time_length=spec_arr.shape[1]) + >>> tone_arr, pitch_arr = deconv_obj.deconv(spec_arr) + + Display Deconv + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_spec + >>> audio_len = audio_arr.shape[0] + >>> fig, ax = plt.subplots() + >>> img = fill_spec(tone_arr, axes=ax, + >>> x_coords=bft_obj.x_coords(audio_len), x_axis='time', + >>> title='Deconv Tone') + >>> fig.colorbar(img, ax=ax) + >>> fig, ax = plt.subplots() + >>> img = fill_spec(pitch_arr, axes=ax, + >>> x_coords=bft_obj.x_coords(audio_len), x_axis='time', + >>> title='Deconv Pitch') + >>> fig.colorbar(img, ax=ax) + """ + + def __init__(self, num): + super(Deconv, self).__init__(pointer(OpaqueDeconv())) + + self.num = num + + fn = self._lib['deconvObj_new'] + fn.argtypes = [POINTER(POINTER(OpaqueDeconv)), c_int] + fn(self._obj, c_int(self.num)) + self._is_created = True + + def set_time_length(self, time_length): + """ + Set time length + + Parameters + ---------- + time_length: int + """ + + fn = self._lib['deconvObj_setTimeLength'] + fn.argtypes = [POINTER(OpaqueDeconv), c_int] + fn(self._obj, c_int(time_length)) + + def deconv(self, m_data_arr): + """ + Compute the spectral deconv feature. + + Parameters + ---------- + m_data_arr: np.ndarray [shape=(fre, time)] + Spectrogram data + + Returns + ------- + m_tone_arr: np.ndarray [shape=(..., time)] + The matrix of tone + + m_pitch_arr: np.ndarray [shape=(..., time)] + The matrix of pitch + """ + if m_data_arr.ndim != 2: + raise ValueError(f'm_data_arr must be 2D array') + m_data_arr = ascontiguous_T(m_data_arr) + + fn = self._lib['deconvObj_deconv'] + fn.argtypes = [POINTER(OpaqueDeconv), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + ] + + shape = m_data_arr.shape # (time, fre) + m_tone_arr = np.zeros(shape, dtype=np.float32) + m_pitch_arr = np.zeros(shape, dtype=np.float32) + fn(self._obj, m_data_arr, m_tone_arr, m_pitch_arr) + + m_tone_arr = ascontiguous_T(m_tone_arr) + m_pitch_arr = ascontiguous_T(m_pitch_arr) + return m_tone_arr, m_pitch_arr + + def __del__(self): + if self._is_created: + fn = self._lib['deconvObj_free'] + fn.argtypes = [POINTER(OpaqueDeconv)] + fn.restype = c_void_p + fn(self._obj) diff --git a/audioflux/feature/extractor.py b/audioflux/feature/extractor.py new file mode 100644 index 0000000..5d20c86 --- /dev/null +++ b/audioflux/feature/extractor.py @@ -0,0 +1,471 @@ +import warnings +from typing import Iterable +from collections import defaultdict + +import numpy as np +from audioflux import BFT, NSGT, CWT, PWT, CQT, ST, FST, DWT, WPT +from audioflux.feature.xxcc import XXCC +from audioflux.feature.spectral import Spectral +from audioflux.feature.deconv import Deconv +from audioflux.type import SpectralFilterBankScaleType, CepstralRectifyType, WindowType, \ + SpectralFilterBankStyleType, SpectralFilterBankNormalType, SpectralDataType, \ + NSGTFilterBankType, WaveletContinueType, WaveletDiscreteType +from audioflux.utils import note_to_hz, check_audio + +__all__ = ['FeatureExtractor'] + + +class FeatureResult(object): + + def __init__(self, name): + self.name = name + self._r = defaultdict(list) + + def items(self): + return self._r.items() + + def __len__(self): + return len(self._r) + + def __getitem__(self, item): + return self._r[item] + + def __repr__(self): + return f'<{self.name}: {self._r.__repr__()}>' + + def __str__(self): + return self.__repr__() + + +class FeatureExtractor(object): + """ + Batch feature extraction + + Parameters + ---------- + transforms: list or str + Transform type. + + Supported: bft/nsgt/cwt/pwt/cqt/st/fst/dwt/wpt + + num: int or None + Number of frequency bins to generate. + + * only `BFT`/`NSGT`/`CWT`/`PWT` available + * cqt is a fixed value of 84 + * dwt see: `DWT` default value + * wpt see: `WPT` default value + + radix2_exp: int + ``fft_length=2**radix2_exp`` + + samplate: int + Sampling rate of the incoming audio. + + low_fre: float or None + Lowest frequency. + + * only `BFT`/`NSGT`/`CWT`/`PWT` available + + high_fre: float or None + Highest frequency. + + * only `BFT`/`NSGT`/`CWT`/`PWT` available + + bin_per_octave: int + Number of bins per octave. + + * only the Octave type of `BFT`/`NSGT`/`CWT`/`PWT` and `CQT` is available + + slide_length: int or None + Window sliding length. + + * only `BFT`/`CQT` available + + scale_type: SpectralFilterBankScaleType + Spectral filter bank type. It determines the type of spectrogram. + + * only `BFT`/`NSGT`/`CWT`/`PWT` available + + See: `type.SpectralFilterBankScaleType` + + wavelet_type: WaveletContinueType + Wavelet Type + + * only `CWT` available + + See: `type.WaveletContinueType` + + Examples + -------- + + Get a 880Hz's audio file + + >>> import audioflux as af + >>> sample_path = af.utils.sample_path('880') + >>> audio_arr, sr = af.read(sample_path) + + Create FeatureExtractor object and extract spectrogram + + >>> from audioflux.type import SpectralFilterBankScaleType + >>> fa_obj = af.FeatureExtractor(transforms=['bft', 'cwt', 'cqt'], samplate=sr, radix2_exp=12, + >>> scale_type=SpectralFilterBankScaleType.OCTAVE) + >>> spec_result = fa_obj.spectrogram(audio_arr, is_continue=True) + + Extract spectral/xxcc/deconv + + >>> spectral_result = fa_obj.spectral(spec_result, spectral='flux', + >>> spectral_kw={'is_positive': True}) + >>> xxcc_result = fa_obj.xxcc(spec_result, cc_num=13) + >>> deconv_result = fa_obj.deconv(spec_result) + """ + _T_BFT = 'bft' + _T_NSGT = 'nsgt' + _T_CWT = 'cwt' + _T_PWT = 'pwt' + _T_CQT = 'cqt' + _T_ST = 'st' + _T_FST = 'fst' + _T_DWT = 'dwt' + _T_WPT = 'wpt' + + def __init__(self, transforms, num=None, radix2_exp=12, samplate=32000, + low_fre=None, high_fre=None, bin_per_octave=12, slide_length=None, + scale_type=SpectralFilterBankScaleType.LINEAR, + wavelet_type=WaveletContinueType.MORSE): + + if radix2_exp > 23: + raise ValueError(f'radix2_exp={radix2_exp} must be less than or equal to 23') + + if num is None: + if scale_type in (SpectralFilterBankScaleType.LINEAR, + SpectralFilterBankScaleType.LINSPACE): + num = (1 << radix2_exp) // 2 + 1 + elif scale_type in (SpectralFilterBankScaleType.MEL, + SpectralFilterBankScaleType.BARK, + SpectralFilterBankScaleType.ERB): + num = 128 + elif scale_type in (SpectralFilterBankScaleType.OCTAVE, + SpectralFilterBankScaleType.LOG): + num = 84 + + self.fft_length = 1 << radix2_exp + self.num = num + self.radix2_exp = radix2_exp + self.samplate = samplate + self.low_fre = low_fre + self.high_fre = high_fre + self.bin_per_octave = bin_per_octave + self.slide_length = slide_length + self.scale_type = scale_type + self.wavelet_type = wavelet_type + + self._transforms = {} + if isinstance(transforms, Iterable): + for name in transforms: + _obj = self._create_obj(name) + self._transforms[name] = _obj + elif isinstance(transforms, str): + _obj = self._create_obj(transforms) + self._transforms[transforms] = _obj + else: + raise ValueError(f'transforms must be type of list or str') + + def _create_obj(self, name): + name = name.lower() + if name == self._T_BFT: + return BFT(num=self.num, radix2_exp=self.radix2_exp, samplate=self.samplate, + low_fre=self.low_fre, high_fre=self.high_fre, + bin_per_octave=self.bin_per_octave, + window_type=WindowType.HANN, slide_length=self.slide_length, + scale_type=self.scale_type, + style_type=SpectralFilterBankStyleType.SLANEY, + normal_type=SpectralFilterBankNormalType.NONE, + data_type=SpectralDataType.MAG, + is_reassign=False, is_temporal=False) + elif name == self._T_NSGT: + return NSGT(num=self.num, radix2_exp=self.radix2_exp, samplate=self.samplate, + low_fre=self.low_fre, high_fre=self.high_fre, + bin_per_octave=self.bin_per_octave, min_len=3, + nsgt_filter_bank_type=NSGTFilterBankType.EFFICIENT, + scale_type=self.scale_type, + style_type=SpectralFilterBankStyleType.SLANEY, + normal_type=SpectralFilterBankNormalType.BAND_WIDTH) + elif name == self._T_CWT: + return CWT(num=self.num, radix2_exp=self.radix2_exp, samplate=self.samplate, + low_fre=self.low_fre, high_fre=self.high_fre, + bin_per_octave=self.bin_per_octave, + wavelet_type=self.wavelet_type, + scale_type=self.scale_type, + is_padding=True) + elif name == self._T_PWT: + return PWT(num=self.num, radix2_exp=self.radix2_exp, samplate=self.samplate, + low_fre=self.low_fre, high_fre=self.high_fre, + bin_per_octave=self.bin_per_octave, + scale_type=self.scale_type, + style_type=SpectralFilterBankStyleType.SLANEY, + normal_type=SpectralFilterBankNormalType.NONE, + is_padding=True) + elif name == self._T_CQT: + return CQT(num=84, samplate=self.samplate, low_fre=note_to_hz('C1'), + bin_per_octave=self.bin_per_octave, + window_type=WindowType.HANN, slide_length=self.slide_length, + normal_type=SpectralFilterBankNormalType.AREA) + elif name == self._T_ST: + # index: 1-2047 + return ST(radix2_exp=self.radix2_exp, samplate=self.samplate) + elif name == self._T_FST: + return FST(radix2_exp=self.radix2_exp, samplate=self.samplate) + elif name == self._T_DWT: + return DWT(num=None, radix2_exp=self.radix2_exp, samplate=self.samplate, + wavelet_type=WaveletDiscreteType.SYM, t1=4, t2=0) + elif name == self._T_WPT: + return WPT(num=None, radix2_exp=self.radix2_exp, samplate=self.samplate, + wavelet_type=WaveletDiscreteType.SYM, t1=4, t2=0) + else: + raise ValueError(f'transform name={name} is not supported') + + # def _continue_spec(self, spec_fn, data_arr): + # + # data_len = data_arr.shape[0] + # win_len = self.fft_length + # win_len_4 = self.fft_length // 4 + # step = win_len_4 * 2 + # win_count = ((data_len - win_len) // step) + 1 + # + # if win_count * step < data_len: + # padding_size = (win_count + 1) * step + win_len - data_len + # data_arr = np.pad(data_arr, (0, padding_size)) + # warnings.warn(f'The length of the last packet of data_arr is not enough ' + # f'for fft_length={self.fft_length} calculation, ' + # f'data_arr will be padded with {padding_size} zeros') + # + # ret_arr = np.array([], dtype=np.float32).reshape(self.num, 0) + # for i in range(win_count): + # sample_arr = data_arr[i * step:i * step + self.fft_length].copy() + # if sample_arr.shape[0] != self.fft_length: + # break + # cur_spec_arr = spec_fn(sample_arr) + # + # start_idx = 0 if i == 0 else win_len_4 + # end_idx = self.fft_length if i == (win_count - 1) else (win_len_4 * 3) + # + # valid_spec_arr = cur_spec_arr[:, start_idx:end_idx] + # ret_arr = np.hstack((ret_arr, valid_spec_arr)) + # return ret_arr + def _continue_spec(self, spec_fn, data_arr): + + ret_arr = None + data_len = data_arr.shape[0] + cur_spec_arr = None + per_time_len = 0 + for i, sample_idx in enumerate(range(0, data_len, self.fft_length // 2)): + sample_arr = data_arr[sample_idx:sample_idx + self.fft_length].copy() + if sample_arr.shape[0] != self.fft_length: + break + cur_spec_arr = spec_fn(sample_arr) + + n_fre, n_time = cur_spec_arr.shape + if n_time < 4: + raise ValueError(f'The length={n_time} of the time dimension of the spectrogram must be greater than 4') + if ret_arr is None: + ret_arr = np.array([], dtype=np.float32).reshape(n_fre, 0) + + per_time_len = n_time // 4 + + start_idx = 0 if i == 0 else per_time_len + + valid_spec_arr = cur_spec_arr[:, start_idx:per_time_len * 3] + ret_arr = np.hstack((ret_arr, valid_spec_arr)) + if cur_spec_arr is not None and per_time_len: + ret_arr = np.hstack((ret_arr, cur_spec_arr[:, per_time_len * 3:])) + return ret_arr + + def _check_name(self, name): + if name not in (self._T_BFT, self._T_NSGT, self._T_CWT, self._T_PWT, + self._T_CQT, self._T_ST, self._T_FST, self._T_DWT, self._T_WPT): + raise ValueError(f'name={name} is not supported') + return True + + def spectrogram(self, data_arr, is_continue=False): + """ + Get the spectrogram of transforms + + Parameters + ---------- + data_arr: np.ndarray [shape=(n,)] or list(np.ndarray [shape=(n,)]) + Input audio datas + + is_continue: bool + Calculate continuous 2D(nsgt/cwt/pwt/st/fst/dwt/wpt) spectrogram. + + Calculate the cwt every fft/2, then divide the x-axis of each cwt into four parts, and + take the middle two parts for splicing (the first part will be spliced with the first + part of the first cwt, and the tail will be spliced with the last part of the last cwt). + + If True, then calculate continuous 2D spectrogram. + If False, the 2D spectrogram can only calculate fft_length(2**radix2_exp) data + + Returns + ------- + out: FeatureResult + spectrogram data + """ + data_arr = np.asarray(data_arr, dtype=np.float32, order='C') + check_audio(data_arr) + + result = FeatureResult('spectrogram') + for name, _obj in self._transforms.items(): + if name == self._T_BFT: + spec_fn = _obj.bft + elif name == self._T_NSGT: + spec_fn = _obj.nsgt + elif name == self._T_CWT: + spec_fn = _obj.cwt + elif name == self._T_PWT: + spec_fn = _obj.pwt + elif name == self._T_CQT: + spec_fn = _obj.cqt + elif name == self._T_ST: + spec_fn = _obj.st + elif name == self._T_FST: + spec_fn = _obj.fst + elif name == self._T_DWT: + spec_fn = lambda x: _obj.dwt(x)[1] + elif name == self._T_WPT: + spec_fn = lambda x: _obj.wpt(x)[1] + else: + raise ValueError(f'name={name} is not supported') + + use_continue = is_continue + if use_continue and name in (self._T_BFT, self._T_CQT): + use_continue = False + + if use_continue: + spec_arr = self._continue_spec(spec_fn, data_arr) + else: + spec_arr = spec_fn(data_arr) + result[name].append(spec_arr) + return result + + def xxcc(self, spec_result, cc_num=13, rectify_type=CepstralRectifyType.LOG, + spec_convert=np.abs): + """ + cepstral coefficients + + See: `XXCC` + + Parameters + ---------- + spec_result: FeatureResult + spectrogram result + + cc_num: int + xxcc num, usually set to 13, 20 or 40 + + rectify_type: CepstralRectifyType + rectify type + + spec_convert: function + Operations on spectrogram data + + Returns + ------- + out: FeatureResult + xxcc data + """ + result = FeatureResult(name='xxcc') + for name, spec_list in spec_result.items(): + if name not in self._transforms: + raise ValueError(f'name={name} not found in transforms') + + _obj = self._transforms[name] + xxcc_obj = XXCC(_obj.num) + for spec in spec_list: + n_fre, n_time = spec.shape + xxcc_obj.set_time_length(n_time) + spec = spec_convert(spec) + xx_arr = xxcc_obj.xxcc(spec, cc_num=cc_num, + rectify_type=rectify_type) + result[name].append(xx_arr) + return result + + def deconv(self, spec_result, spec_convert=np.abs): + """ + Deconv feature + + See: `Deconv` + + Parameters + ---------- + spec_result: FeatureResult + spectrogram result + + spec_convert: function + Operations on spectrogram data + + Returns + ------- + out: FeatureResult + deconv data + """ + result = FeatureResult('deconv') + for name, spec_list in spec_result.items(): + if name not in self._transforms: + raise ValueError(f'name={name} not found in transforms') + + _obj = self._transforms[name] + deconv_obj = Deconv(_obj.num) + n_fre, n_time = spec_list[0].shape + deconv_obj.set_time_length(n_time) + for spec in spec_list: + spec = spec_convert(spec) + xx_arr = deconv_obj.deconv(spec) + result[name].append(xx_arr) + return result + + def spectral(self, spec_result, spectral, spectral_kw=None, spec_convert=np.abs): + """ + spectral related features + + Parameters + ---------- + spec_result: FeatureResult + spectrogram result + + spectral: str + spectral feature name + + See: `Spectral` + + spectral_kw: dict or None + spectral feature parameters + + See: `Spectral` + + spec_convert: function + Operations on spectrogram data + + Returns + ------- + out: FeatureResult + spectral data + """ + result = FeatureResult(f'spectral.{spectral}') + for name, spec_list in spec_result.items(): + if name not in self._transforms: + raise ValueError(f'name={name} not found in transforms') + + _obj = self._transforms[name] + spectral_obj = Spectral(num=_obj.num, + fre_band_arr=_obj.get_fre_band_arr()) + if not hasattr(spectral_obj, spectral): + raise ValueError(f'spectral={spectral} function is not found') + n_fre, n_time = spec_list[0].shape + spectral_obj.set_time_length(n_time) + for spec in spec_list: + spec = spec_convert(spec) + spectral_kw = {} if spectral_kw is None else spectral_kw + spectral_arr = getattr(spectral_obj, spectral)(spec, **spectral_kw) + result[name].append(spectral_arr) + return result diff --git a/audioflux/feature/spectral.py b/audioflux/feature/spectral.py new file mode 100644 index 0000000..be04ccc --- /dev/null +++ b/audioflux/feature/spectral.py @@ -0,0 +1,2321 @@ +import numpy as np +import ctypes +from ctypes import Structure, POINTER, pointer, c_int, c_float, c_void_p, c_size_t +from audioflux.type import SpectralNoveltyMethodType, SpectralNoveltyDataType +from audioflux.base import Base +from audioflux.utils import ascontiguous_T + +__all__ = ["Spectral"] + + +class OpaqueSpectral(Structure): + _fields_ = [] + + +class Spectral(Base): + """ + Spectrum feature, supports all spectrum types. + + Parameters + ---------- + num: int + Number of frequency bins to generate. It must be the same as the + `num` parameter of the transformation (same as the spectrogram matrix). + + fre_band_arr: np.ndarray [shape=(n_fre,)] + The array of frequency bands. Obtained by calling the `get_fre_band_arr()` method of the transformation. + """ + + def __init__(self, num, fre_band_arr): + super(Spectral, self).__init__(pointer(OpaqueSpectral())) + + self.num = num + self.fre_band_arr = np.asarray(fre_band_arr, dtype=np.float32) + + fn = self._lib['spectralObj_new'] + fn.argtypes = [POINTER(POINTER(OpaqueSpectral)), + c_int, + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS'), + ] + fn(self._obj, c_int(self.num), self.fre_band_arr) + self._is_created = True + + def set_time_length(self, time_length): + """ + Set time length + + Parameters + ---------- + time_length: int + """ + + fn = self._lib['spectralObj_setTimeLength'] + fn.argtypes = [ + POINTER(OpaqueSpectral), + c_int + ] + fn(self._obj, c_int(time_length)) + + def set_edge(self, start: int, end: int): + """ + Set edge + + Parameters + ---------- + start: int + 0 ~ end + end: int + start ~ num-1 + """ + if not 0 <= start < end: + raise ValueError(f'start={start} must be in range [0, {end})') + if not start < end <= self.num - 1: + raise ValueError(f'start={end} must be in range ({start}, {self.num - 1}]') + + fn = self._lib['spectralObj_setEdge'] + fn.argtypes = [ + POINTER(OpaqueSpectral), + c_int, + c_int, + ] + fn(self._obj, c_int(start), c_int(end)) + + def set_edge_arr(self, index_arr): + """ + Set edge array + + Parameters + ---------- + index_arr: np.ndarray [shape=(n,)] + fre index array + """ + + fn = self._lib['spectralObj_setEdgeArr'] + fn.argtypes = [ + POINTER(OpaqueSpectral), + POINTER(c_int), + c_int + ] + + index_len = len(index_arr) + + calloc_fn = self._lib['calloc'] + calloc_fn.argtypes = [c_size_t, c_size_t] + calloc_fn.restype = c_void_p + address = calloc_fn(c_size_t(index_len), c_size_t(ctypes.sizeof(c_int))) + + p_index_arr = (c_int * index_len).from_address(address) + for i, v in enumerate(index_arr): + p_index_arr[i] = v + p_index_arr = ctypes.cast(p_index_arr, POINTER(c_int)) + + fn(self._obj, p_index_arr, c_int(index_len)) + + def flatness(self, m_data_arr): + """ + Compute the spectral flatness feature. + + :math:`\qquad flatness=\\frac{\\left ( \prod_{k=b_1}^{b_2} s_k \\right)^{ \\frac{1}{b_2-b_1} } } {\\frac{1}{b_2-b_1} \sum_{ k=b_1 }^{b_2} s_k}` + + * :math:`b_1` and :math:`b_2`: the frequency band bin boundaries + * :math:`s_k`: the spectrum value, which can be magnitude spectrum or power spectrum + + Parameters + ---------- + m_data_arr: np.ndarray [shape=(fre, time)] + Spectrogram data + + Returns + ------- + flatness: np.ndarray [shape=(time,)] + flatness frequency for each time + + Examples + -------- + + Read chord audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('chord') + >>> audio_arr, sr = af.read(audio_path) + + Create BFT-Linear object and extract spectrogram + + >>> from audioflux.type import SpectralFilterBankScaleType, SpectralDataType + >>> import numpy as np + >>> bft_obj = af.BFT(num=2049, samplate=sr, radix2_exp=12, slide_length=1024, + >>> data_type=SpectralDataType.MAG, + >>> scale_type=SpectralFilterBankScaleType.LINEAR) + >>> spec_arr = bft_obj.bft(audio_arr) + >>> spec_arr = np.abs(spec_arr) + + Create Spectral object and extract flatness feature + + >>> spectral_obj = af.Spectral(num=bft_obj.num, + >>> fre_band_arr=bft_obj.get_fre_band_arr()) + >>> n_time = spec_arr.shape[1] # Or use bft_obj.cal_time_length(len(audio_arr)) + >>> spectral_obj.set_time_length(n_time) + >>> flatness_arr = spectral_obj.flatness(spec_arr) + + Display plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_plot, fill_wave + >>> fig, ax = plt.subplots(nrows=2, sharex=True) + >>> fill_wave(audio_arr, samplate=sr, axes=ax[0]) + >>> times = np.arange(0, len(flatness_arr)) * (bft_obj.slide_length / bft_obj.samplate) + >>> fill_plot(times, flatness_arr, axes=ax[1], label='flatness') + """ + m_data_arr = ascontiguous_T(m_data_arr, dtype=np.float32) + + fn = self._lib['spectralObj_flatness'] + fn.argtypes = [ + POINTER(OpaqueSpectral), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS') + ] + n_len, m_len = m_data_arr.shape # t * f + ret = np.zeros(n_len, dtype=np.float32) + fn(self._obj, m_data_arr, ret) + return ret + + def flux(self, m_data_arr, step=1, p=2, is_positive=False, is_no_exp=True, tp=0): + """ + Compute the spectral flux feature. + + :math:`\qquad flux(t)=\\left( \sum_{k=b_1}^{b_2} |s_k(t)-s_k(t-1) |^{p} \\right)^{\\frac{1}{p}}` + + * :math:`b_1` and :math:`b_2`: the frequency band bin boundaries + * :math:`s_k`: the spectrum value, which can be magnitude spectrum or power spectrum + + * In general :math:`s_k(t) \geq s_k(t-1)` participate in the calculation + + Parameters + ---------- + m_data_arr: np.ndarray [shape=(fre, time)] + Spectrogram data + + step: int + Compute time axis steps, like 1/2/3/... + + p: int, 1 or 2 + norm: 1 abs; 2 pow + + is_positive: bool + Whether to set negative numbers to 0 + + is_no_exp: bool + Whether to exp + + tp: int, 0 or 1 + 0 sum 1 mean + + Returns + ------- + flux: np.ndarray [shape=(time,)] + flux frequency for each time + + Examples + -------- + + Read chord audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('chord') + >>> audio_arr, sr = af.read(audio_path) + + Create BFT-Linear object and extract spectrogram + + >>> from audioflux.type import SpectralFilterBankScaleType, SpectralDataType + >>> import numpy as np + >>> bft_obj = af.BFT(num=2049, samplate=sr, radix2_exp=12, slide_length=1024, + >>> data_type=SpectralDataType.MAG, + >>> scale_type=SpectralFilterBankScaleType.LINEAR) + >>> spec_arr = bft_obj.bft(audio_arr) + >>> spec_arr = np.abs(spec_arr) + + Create Spectral object and extract flux feature + + >>> spectral_obj = af.Spectral(num=bft_obj.num, + >>> fre_band_arr=bft_obj.get_fre_band_arr()) + >>> n_time = spec_arr.shape[1] # Or use bft_obj.cal_time_length(len(audio_arr)) + >>> spectral_obj.set_time_length(n_time) + >>> flux_arr = spectral_obj.flux(spec_arr) + + Display plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_plot, fill_wave + >>> fig, ax = plt.subplots(nrows=2, sharex=True) + >>> fill_wave(audio_arr, samplate=sr, axes=ax[0]) + >>> times = np.arange(0, len(flux_arr)) * (bft_obj.slide_length / bft_obj.samplate) + >>> fill_plot(times, flux_arr, axes=ax[1], label='flux') + """ + m_data_arr = ascontiguous_T(m_data_arr, dtype=np.float32) + + fn = self._lib['spectralObj_flux'] + fn.argtypes = [ + POINTER(OpaqueSpectral), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + c_int, c_float, c_int, + POINTER(c_int), POINTER(c_int), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS') + ] + time_len, _ = m_data_arr.shape # t * n + ret = np.zeros(time_len, dtype=np.float32) + fn(self._obj, + m_data_arr, + c_int(step), + c_float(p), + c_int(int(is_positive)), + pointer(c_int(int(is_no_exp))), + pointer(c_int(tp)), + ret) + return ret + + def rolloff(self, m_data_arr, threshold=0.95): + """ + Compute the spectral rolloff feature. + + :math:`\qquad \sum_{k=b_1}^{i}|s_k| \geq \eta \sum_{k=b_1}^{b_2}s_k` + + * :math:`b_1` and :math:`b_2`: the frequency band bin boundaries + * :math:`s_k`: the spectrum value, which can be magnitude spectrum or power spectrum + * :math:`\eta \in (0,1)`, generally take 0.95 or 0.85, satisfy the condition :math:`i` get :math:`f_i` rolloff frequency + + Parameters + ---------- + m_data_arr: np.ndarray [shape=(fre, time)] + Spectrogram data + + threshold: float, [0,1] + rolloff threshold. Generally take 0.95 or 0.85. + + Returns + ------- + rolloff: np.ndarray [shape=(time,)] + rolloff frequency for each time + + Examples + -------- + + Read chord audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('chord') + >>> audio_arr, sr = af.read(audio_path) + + Create BFT-Linear object and extract spectrogram + + >>> from audioflux.type import SpectralFilterBankScaleType, SpectralDataType + >>> import numpy as np + >>> bft_obj = af.BFT(num=2049, samplate=sr, radix2_exp=12, slide_length=1024, + >>> data_type=SpectralDataType.MAG, + >>> scale_type=SpectralFilterBankScaleType.LINEAR) + >>> spec_arr = bft_obj.bft(audio_arr) + >>> spec_arr = np.abs(spec_arr) + + Create Spectral object and extract rolloff feature + + >>> spectral_obj = af.Spectral(num=bft_obj.num, + >>> fre_band_arr=bft_obj.get_fre_band_arr()) + >>> n_time = spec_arr.shape[1] # Or use bft_obj.cal_time_length(len(audio_arr)) + >>> spectral_obj.set_time_length(n_time) + >>> rolloff_arr = spectral_obj.rolloff(spec_arr) + + Display plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_plot, fill_wave + >>> fig, ax = plt.subplots(nrows=2, sharex=True) + >>> fill_wave(audio_arr, samplate=sr, axes=ax[0]) + >>> times = np.arange(0, len(rolloff_arr)) * (bft_obj.slide_length / bft_obj.samplate) + >>> fill_plot(times, rolloff_arr, axes=ax[1], label='rolloff') + """ + if not 0 <= threshold <= 1: + raise ValueError(f'threshold must be 0 or 1') + + m_data_arr = ascontiguous_T(m_data_arr, dtype=np.float32) + + fn = self._lib['spectralObj_rolloff'] + fn.argtypes = [ + POINTER(OpaqueSpectral), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + c_float, + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS') + ] + n_len, m_len = m_data_arr.shape # t * n + ret = np.zeros(n_len, dtype=np.float32) + fn(self._obj, m_data_arr, c_float(threshold), ret) + return ret + + def centroid(self, m_data_arr): + """ + Compute the spectral centroid feature. + + :math:`\qquad \mu_1=\\frac{\sum_{ k=b_1 }^{b_2} f_ks_k } {\sum_{k=b_1}^{b_2} s_k }` + + * :math:`b_1` and :math:`b_2`: the frequency band bin boundaries + * :math:`f_k` is in Hz + * :math:`s_k`: the spectrum value, which can be magnitude spectrum or power spectrum + + Parameters + ---------- + m_data_arr: np.ndarray [shape=(fre, time)] + Spectrogram data + + Returns + ------- + centroid: np.ndarray [shape=(time,)] + centroid frequency for each time + + Examples + -------- + + Read chord audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('chord') + >>> audio_arr, sr = af.read(audio_path) + + Create BFT-Linear object and extract spectrogram + + >>> from audioflux.type import SpectralFilterBankScaleType, SpectralDataType + >>> import numpy as np + >>> bft_obj = af.BFT(num=2049, samplate=sr, radix2_exp=12, slide_length=1024, + >>> data_type=SpectralDataType.MAG, + >>> scale_type=SpectralFilterBankScaleType.LINEAR) + >>> spec_arr = bft_obj.bft(audio_arr) + >>> spec_arr = np.abs(spec_arr) + + Create Spectral object and extract centroid feature + + >>> spectral_obj = af.Spectral(num=bft_obj.num, + >>> fre_band_arr=bft_obj.get_fre_band_arr()) + >>> n_time = spec_arr.shape[1] # Or use bft_obj.cal_time_length(len(audio_arr)) + >>> spectral_obj.set_time_length(n_time) + >>> centroid_arr = spectral_obj.centroid(spec_arr) + + Display plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_plot, fill_wave + >>> fig, ax = plt.subplots(nrows=2, sharex=True) + >>> fill_wave(audio_arr, samplate=sr, axes=ax[0]) + >>> times = np.arange(0, len(centroid_arr)) * (bft_obj.slide_length / bft_obj.samplate) + >>> fill_plot(times, centroid_arr, axes=ax[1], label='centroid') + """ + m_data_arr = ascontiguous_T(m_data_arr, dtype=np.float32) + + fn = self._lib['spectralObj_centroid'] + fn.argtypes = [ + POINTER(OpaqueSpectral), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS') + ] + n_len, m_len = m_data_arr.shape # t * n + ret = np.zeros(n_len, dtype=np.float32) + fn(self._obj, m_data_arr, ret) + return ret + + def spread(self, m_data_arr): + """ + Compute the spectral spread feature. + + :math:`\qquad \mu_2=\sqrt{\\frac{\sum_{ k=b_1 }^{b_2} (f_k-\mu_1)^2 s_k } {\sum_{k=b_1}^{b_2} s_k } }` + + * :math:`b_1` and :math:`b_2`: the frequency band bin boundaries + * :math:`f_k` is in Hz + * :math:`s_k`: the spectrum value, which can be magnitude spectrum or power spectrum + * :math:`u_1`: `Spectral.centroid` + + Parameters + ---------- + m_data_arr: np.ndarray [shape=(fre, time)] + Spectrogram data + + Returns + ------- + spread: np.ndarray [shape=(time,)] + spread frequency for each time + + Examples + -------- + + Read chord audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('chord') + >>> audio_arr, sr = af.read(audio_path) + + Create BFT-Linear object and extract spectrogram + + >>> from audioflux.type import SpectralFilterBankScaleType, SpectralDataType + >>> import numpy as np + >>> bft_obj = af.BFT(num=2049, samplate=sr, radix2_exp=12, slide_length=1024, + >>> data_type=SpectralDataType.MAG, + >>> scale_type=SpectralFilterBankScaleType.LINEAR) + >>> spec_arr = bft_obj.bft(audio_arr) + >>> spec_arr = np.abs(spec_arr) + + Create Spectral object and extract spread feature + + >>> spectral_obj = af.Spectral(num=bft_obj.num, + >>> fre_band_arr=bft_obj.get_fre_band_arr()) + >>> n_time = spec_arr.shape[1] # Or use bft_obj.cal_time_length(len(audio_arr)) + >>> spectral_obj.set_time_length(n_time) + >>> spread_arr = spectral_obj.spread(spec_arr) + + Display plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_plot, fill_wave + >>> fig, ax = plt.subplots(nrows=2, sharex=True) + >>> fill_wave(audio_arr, samplate=sr, axes=ax[0]) + >>> times = np.arange(0, len(spread_arr)) * (bft_obj.slide_length / bft_obj.samplate) + >>> fill_plot(times, spread_arr, axes=ax[1], label='spread') + """ + m_data_arr = ascontiguous_T(m_data_arr, dtype=np.float32) + + fn = self._lib['spectralObj_spread'] + fn.argtypes = [ + POINTER(OpaqueSpectral), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS') + ] + n_len, m_len = m_data_arr.shape # t * n + ret = np.zeros(n_len, dtype=np.float32) + fn(self._obj, m_data_arr, ret) + return ret + + def skewness(self, m_data_arr): + """ + Compute the spectral skewness feature. + + :math:`\qquad \mu_3=\\frac{\sum_{ k=b_1 }^{b_2} (f_k-\mu_1)^3 s_k } {(\mu_2)^3 \sum_{k=b_1}^{b_2} s_k }` + + * :math:`b_1` and :math:`b_2`: the frequency band bin boundaries + * :math:`f_k` is in Hz + * :math:`s_k`: the spectrum value, which can be magnitude spectrum or power spectrum + * :math:`u_1`: `Spectral.centroid` + * :math:`u_2`: `Spectral.spread` + + Parameters + ---------- + m_data_arr: np.ndarray [shape=(fre, time)] + Spectrogram data + + Returns + ------- + skewness: np.ndarray [shape=(time,)] + skewness frequency for each time + + Examples + -------- + + Read chord audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('chord') + >>> audio_arr, sr = af.read(audio_path) + + Create BFT-Linear object and extract spectrogram + + >>> from audioflux.type import SpectralFilterBankScaleType, SpectralDataType + >>> import numpy as np + >>> bft_obj = af.BFT(num=2049, samplate=sr, radix2_exp=12, slide_length=1024, + >>> data_type=SpectralDataType.MAG, + >>> scale_type=SpectralFilterBankScaleType.LINEAR) + >>> spec_arr = bft_obj.bft(audio_arr) + >>> spec_arr = np.abs(spec_arr) + + Create Spectral object and extract skewness feature + + >>> spectral_obj = af.Spectral(num=bft_obj.num, + >>> fre_band_arr=bft_obj.get_fre_band_arr()) + >>> n_time = spec_arr.shape[1] # Or use bft_obj.cal_time_length(len(audio_arr)) + >>> spectral_obj.set_time_length(n_time) + >>> skewness_arr = spectral_obj.skewness(spec_arr) + + Display plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_plot, fill_wave + >>> fig, ax = plt.subplots(nrows=2, sharex=True) + >>> fill_wave(audio_arr, samplate=sr, axes=ax[0]) + >>> times = np.arange(0, len(skewness_arr)) * (bft_obj.slide_length / bft_obj.samplate) + >>> fill_plot(times, skewness_arr, axes=ax[1], label='skewness') + """ + m_data_arr = ascontiguous_T(m_data_arr, dtype=np.float32) + + fn = self._lib['spectralObj_skewness'] + fn.argtypes = [ + POINTER(OpaqueSpectral), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS') + ] + n_len, m_len = m_data_arr.shape # t * n + ret = np.zeros(n_len, dtype=np.float32) + fn(self._obj, m_data_arr, ret) + return ret + + def kurtosis(self, m_data_arr): + """ + Compute the spectral kurtosis feature. + + :math:`\qquad \mu_4=\\frac{\sum_{ k=b_1 }^{b_2} (f_k-\mu_1)^4 s_k } {(\mu_2)^4 \sum_{k=b_1}^{b_2} s_k }` + + * :math:`b_1` and :math:`b_2`: the frequency band bin boundaries + * :math:`f_k` is in Hz + * :math:`s_k`: the spectrum value, which can be magnitude spectrum or power spectrum + * :math:`u_1`: `Spectral.centroid` + * :math:`u_2`: `Spectral.spread` + + Parameters + ---------- + m_data_arr: np.ndarray [shape=(fre, time)] + Spectrogram data + + Returns + ------- + kurtosis: np.ndarray [shape=(time,)] + kurtosis frequency for each time + + Examples + -------- + + Read chord audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('chord') + >>> audio_arr, sr = af.read(audio_path) + + Create BFT-Linear object and extract spectrogram + + >>> from audioflux.type import SpectralFilterBankScaleType, SpectralDataType + >>> import numpy as np + >>> bft_obj = af.BFT(num=2049, samplate=sr, radix2_exp=12, slide_length=1024, + >>> data_type=SpectralDataType.MAG, + >>> scale_type=SpectralFilterBankScaleType.LINEAR) + >>> spec_arr = bft_obj.bft(audio_arr) + >>> spec_arr = np.abs(spec_arr) + + Create Spectral object and extract kurtosis feature + + >>> spectral_obj = af.Spectral(num=bft_obj.num, + >>> fre_band_arr=bft_obj.get_fre_band_arr()) + >>> n_time = spec_arr.shape[1] # Or use bft_obj.cal_time_length(len(audio_arr)) + >>> spectral_obj.set_time_length(n_time) + >>> kurtosis_arr = spectral_obj.kurtosis(spec_arr) + + Display plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_plot, fill_wave + >>> fig, ax = plt.subplots(nrows=2, sharex=True) + >>> fill_wave(audio_arr, samplate=sr, axes=ax[0]) + >>> times = np.arange(0, len(kurtosis_arr)) * (bft_obj.slide_length / bft_obj.samplate) + >>> fill_plot(times, kurtosis_arr, axes=ax[1], label='kurtosis') + """ + m_data_arr = ascontiguous_T(m_data_arr, dtype=np.float32) + + fn = self._lib['spectralObj_kurtosis'] + fn.argtypes = [ + POINTER(OpaqueSpectral), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS') + ] + n_len, m_len = m_data_arr.shape # t * n + ret = np.zeros(n_len, dtype=np.float32) + fn(self._obj, m_data_arr, ret) + return ret + + def entropy(self, m_data_arr, is_norm=False): + """ + Compute the spectral entropy feature. + + Set: :math:`p_k=\\frac{s_k}{\sum_{k=b_1}^{b_2}s_k}` + + :math:`\qquad entropy1= \\frac{-\sum_{ k=b_1 }^{b_2} p_k \log(p_k)} {\log(b_2-b_1)}` + + Or + + :math:`\qquad entropy2= {-\sum_{ k=b_1 }^{b_2} p_k \log(p_k)}` + + * :math:`b_1` and :math:`b_2`: the frequency band bin boundaries + * :math:`s_k`: the spectrum value, which can be magnitude spectrum or power spectrum + + Parameters + ---------- + m_data_arr: np.ndarray [shape=(fre, time)] + Spectrogram data + + is_norm: bool + Whether to norm + + Returns + ------- + entropy: np.ndarray [shape=(time,)] + entropy frequency for each time + + Examples + -------- + + Read chord audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('chord') + >>> audio_arr, sr = af.read(audio_path) + + Create BFT-Linear object and extract spectrogram + + >>> from audioflux.type import SpectralFilterBankScaleType, SpectralDataType + >>> import numpy as np + >>> bft_obj = af.BFT(num=2049, samplate=sr, radix2_exp=12, slide_length=1024, + >>> data_type=SpectralDataType.MAG, + >>> scale_type=SpectralFilterBankScaleType.LINEAR) + >>> spec_arr = bft_obj.bft(audio_arr) + >>> spec_arr = np.abs(spec_arr) + + Create Spectral object and extract entropy feature + + >>> spectral_obj = af.Spectral(num=bft_obj.num, + >>> fre_band_arr=bft_obj.get_fre_band_arr()) + >>> n_time = spec_arr.shape[1] # Or use bft_obj.cal_time_length(len(audio_arr)) + >>> spectral_obj.set_time_length(n_time) + >>> entropy_arr = spectral_obj.entropy(spec_arr) + + Display plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_plot, fill_wave + >>> fig, ax = plt.subplots(nrows=2, sharex=True) + >>> fill_wave(audio_arr, samplate=sr, axes=ax[0]) + >>> times = np.arange(0, len(entropy_arr)) * (bft_obj.slide_length / bft_obj.samplate) + >>> fill_plot(times, entropy_arr, axes=ax[1], label='entropy') + """ + m_data_arr = ascontiguous_T(m_data_arr, dtype=np.float32) + + fn = self._lib['spectralObj_entropy'] + fn.argtypes = [ + POINTER(OpaqueSpectral), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + c_int, + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS') + ] + n_len, m_len = m_data_arr.shape # t * n + ret = np.zeros(n_len, dtype=np.float32) + fn(self._obj, m_data_arr, c_int(int(is_norm)), ret) + return ret + + def crest(self, m_data_arr): + """ + Compute the spectral crest feature. + + :math:`\qquad crest =\\frac{max(s_{k\in_{[b_1,b_2]} }) } {\\frac{1}{b_2-b_1} \sum_{ k=b_1 }^{b_2} s_k}` + + * :math:`b_1` and :math:`b_2`: the frequency band bin boundaries + * :math:`s_k`: the spectrum value, which can be magnitude spectrum or power spectrum + + Parameters + ---------- + m_data_arr: np.ndarray [shape=(fre, time)] + Spectrogram data + + Returns + ------- + crest: np.ndarray [shape=(time,)] + crest frequency for each time + + Examples + -------- + + Read chord audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('chord') + >>> audio_arr, sr = af.read(audio_path) + + Create BFT-Linear object and extract spectrogram + + >>> from audioflux.type import SpectralFilterBankScaleType, SpectralDataType + >>> import numpy as np + >>> bft_obj = af.BFT(num=2049, samplate=sr, radix2_exp=12, slide_length=1024, + >>> data_type=SpectralDataType.MAG, + >>> scale_type=SpectralFilterBankScaleType.LINEAR) + >>> spec_arr = bft_obj.bft(audio_arr) + >>> spec_arr = np.abs(spec_arr) + + Create Spectral object and extract crest feature + + >>> spectral_obj = af.Spectral(num=bft_obj.num, + >>> fre_band_arr=bft_obj.get_fre_band_arr()) + >>> n_time = spec_arr.shape[1] # Or use bft_obj.cal_time_length(len(audio_arr)) + >>> spectral_obj.set_time_length(n_time) + >>> crest_arr = spectral_obj.crest(spec_arr) + + Display plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_plot, fill_wave + >>> fig, ax = plt.subplots(nrows=2, sharex=True) + >>> fill_wave(audio_arr, samplate=sr, axes=ax[0]) + >>> times = np.arange(0, len(crest_arr)) * (bft_obj.slide_length / bft_obj.samplate) + >>> fill_plot(times, crest_arr, axes=ax[1], label='crest') + """ + m_data_arr = ascontiguous_T(m_data_arr, dtype=np.float32) + + fn = self._lib['spectralObj_crest'] + fn.argtypes = [ + POINTER(OpaqueSpectral), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS') + ] + n_len, m_len = m_data_arr.shape # t * n + ret = np.zeros(n_len, dtype=np.float32) + fn(self._obj, m_data_arr, ret) + return ret + + def slope(self, m_data_arr): + """ + Compute the spectral slope feature. + + :math:`\qquad slope=\\frac{ \sum_{k=b_1}^{b_2}(f_k-\mu_f)(s_k-\mu_s) } { \sum_{k=b_1}^{b_2}(f_k-\mu_f)^2 }` + + * :math:`b_1` and :math:`b_2`: the frequency band bin boundaries + * :math:`f_k` is in Hz + * :math:`s_k`: the spectrum value, which can be magnitude spectrum or power spectrum + * :math:`\mu_f`: average frequency value + * :math:`\mu_s`: average spectral value + + Parameters + ---------- + m_data_arr: np.ndarray [shape=(fre, time)] + Spectrogram data + + Returns + ------- + slope: np.ndarray [shape=(time,)] + slope frequency for each time + + Examples + -------- + + Read chord audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('chord') + >>> audio_arr, sr = af.read(audio_path) + + Create BFT-Linear object and extract spectrogram + + >>> from audioflux.type import SpectralFilterBankScaleType, SpectralDataType + >>> import numpy as np + >>> bft_obj = af.BFT(num=2049, samplate=sr, radix2_exp=12, slide_length=1024, + >>> data_type=SpectralDataType.MAG, + >>> scale_type=SpectralFilterBankScaleType.LINEAR) + >>> spec_arr = bft_obj.bft(audio_arr) + >>> spec_arr = np.abs(spec_arr) + + Create Spectral object and extract slope feature + + >>> spectral_obj = af.Spectral(num=bft_obj.num, + >>> fre_band_arr=bft_obj.get_fre_band_arr()) + >>> n_time = spec_arr.shape[1] # Or use bft_obj.cal_time_length(len(audio_arr)) + >>> spectral_obj.set_time_length(n_time) + >>> slope_arr = spectral_obj.slope(spec_arr) + + Display plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_plot, fill_wave + >>> fig, ax = plt.subplots(nrows=2, sharex=True) + >>> fill_wave(audio_arr, samplate=sr, axes=ax[0]) + >>> times = np.arange(0, len(slope_arr)) * (bft_obj.slide_length / bft_obj.samplate) + >>> fill_plot(times, slope_arr, axes=ax[1], label='slope') + """ + m_data_arr = ascontiguous_T(m_data_arr, dtype=np.float32) + + fn = self._lib['spectralObj_slope'] + fn.argtypes = [ + POINTER(OpaqueSpectral), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS') + ] + n_len, m_len = m_data_arr.shape # t * n + ret = np.zeros(n_len, dtype=np.float32) + fn(self._obj, m_data_arr, ret) + return ret + + def decrease(self, m_data_arr): + """ + Compute the spectral decrease feature. + + :math:`\qquad decrease=\\frac { \sum_{k=b_1+1}^{b_2} \\frac {s_k-s_{b_1}}{k-1} } { \sum_{k=b_1+1}^{b_2} s_k }` + + * :math:`b_1` and :math:`b_2`: the frequency band bin boundaries + * :math:`s_k`: the spectrum value, which can be magnitude spectrum or power spectrum + + Parameters + ---------- + m_data_arr: np.ndarray [shape=(fre, time)] + Spectrogram data + + Returns + ------- + decrease: np.ndarray [shape=(time,)] + decrease frequency for each time + + Examples + -------- + + Read chord audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('chord') + >>> audio_arr, sr = af.read(audio_path) + + Create BFT-Linear object and extract spectrogram + + >>> from audioflux.type import SpectralFilterBankScaleType, SpectralDataType + >>> import numpy as np + >>> bft_obj = af.BFT(num=2049, samplate=sr, radix2_exp=12, slide_length=1024, + >>> data_type=SpectralDataType.MAG, + >>> scale_type=SpectralFilterBankScaleType.LINEAR) + >>> spec_arr = bft_obj.bft(audio_arr) + >>> spec_arr = np.abs(spec_arr) + + Create Spectral object and extract decrease feature + + >>> spectral_obj = af.Spectral(num=bft_obj.num, + >>> fre_band_arr=bft_obj.get_fre_band_arr()) + >>> n_time = spec_arr.shape[1] # Or use bft_obj.cal_time_length(len(audio_arr)) + >>> spectral_obj.set_time_length(n_time) + >>> decrease_arr = spectral_obj.decrease(spec_arr) + + Display plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_plot, fill_wave + >>> fig, ax = plt.subplots(nrows=2, sharex=True) + >>> fill_wave(audio_arr, samplate=sr, axes=ax[0]) + >>> times = np.arange(0, len(decrease_arr)) * (bft_obj.slide_length / bft_obj.samplate) + >>> fill_plot(times, decrease_arr, axes=ax[1], label='decrease') + """ + m_data_arr = ascontiguous_T(m_data_arr, dtype=np.float32) + + fn = self._lib['spectralObj_decrease'] + fn.argtypes = [ + POINTER(OpaqueSpectral), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS') + ] + n_len, m_len = m_data_arr.shape # t * n + ret = np.zeros(n_len, dtype=np.float32) + fn(self._obj, m_data_arr, ret) + return ret + + def band_width(self, m_data_arr, p=2): + """ + Compute the spectral band_width feature. + + :math:`\qquad bandwidth=\\left(\sum_{k=b_1}^{b_2} s_k(f_k-centroid)^p \\right)^{\\frac{1}{p}}` + + * :math:`b_1` and :math:`b_2`: the frequency band bin boundaries + * :math:`f_k` is in Hz + * :math:`s_k`: the spectrum value, which can be magnitude spectrum or power spectrum + * centroid: `Spectral.centroid` + + Parameters + ---------- + m_data_arr: np.ndarray [shape=(fre, time)] + Spectrogram data + + p: int, 1 or 2 + norm: 1 abs; 2 pow + + Returns + ------- + band_width: np.ndarray [shape=(time,)] + band_width frequency for each time + + Examples + -------- + + Read chord audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('chord') + >>> audio_arr, sr = af.read(audio_path) + + Create BFT-Linear object and extract spectrogram + + >>> from audioflux.type import SpectralFilterBankScaleType, SpectralDataType + >>> import numpy as np + >>> bft_obj = af.BFT(num=2049, samplate=sr, radix2_exp=12, slide_length=1024, + >>> data_type=SpectralDataType.MAG, + >>> scale_type=SpectralFilterBankScaleType.LINEAR) + >>> spec_arr = bft_obj.bft(audio_arr) + >>> spec_arr = np.abs(spec_arr) + + Create Spectral object and extract band_width feature + + >>> spectral_obj = af.Spectral(num=bft_obj.num, + >>> fre_band_arr=bft_obj.get_fre_band_arr()) + >>> n_time = spec_arr.shape[1] # Or use bft_obj.cal_time_length(len(audio_arr)) + >>> spectral_obj.set_time_length(n_time) + >>> band_width_arr = spectral_obj.band_width(spec_arr) + + Display plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_plot, fill_wave + >>> fig, ax = plt.subplots(nrows=2, sharex=True) + >>> fill_wave(audio_arr, samplate=sr, axes=ax[0]) + >>> times = np.arange(0, len(band_width_arr)) * (bft_obj.slide_length / bft_obj.samplate) + >>> fill_plot(times, band_width_arr, axes=ax[1], label='band_width') + """ + m_data_arr = ascontiguous_T(m_data_arr, dtype=np.float32) + + fn = self._lib['spectralObj_bandWidth'] + fn.argtypes = [ + POINTER(OpaqueSpectral), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + c_float, + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS') + ] + n_len, m_len = m_data_arr.shape # t * n + ret = np.zeros(n_len, dtype=np.float32) + fn(self._obj, m_data_arr, c_float(p), ret) + return ret + + def rms(self, m_data_arr): + """ + Compute the spectral rms feature. + + :math:`\qquad rms=\sqrt{ \\frac{1}{N} \sum_{n=1}^N x^2[n] }=\sqrt {\\frac{1}{N^2}\sum_{m=1}^N |X[m]|^2 }` + + Parameters + ---------- + m_data_arr: np.ndarray [shape=(fre, time)] + Spectrogram data + + Returns + ------- + rms: np.ndarray [shape=(time,)] + rms frequency for each time + + Examples + -------- + + Read chord audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('chord') + >>> audio_arr, sr = af.read(audio_path) + + Create BFT-Linear object and extract spectrogram + + >>> from audioflux.type import SpectralFilterBankScaleType, SpectralDataType + >>> import numpy as np + >>> bft_obj = af.BFT(num=2049, samplate=sr, radix2_exp=12, slide_length=1024, + >>> data_type=SpectralDataType.MAG, + >>> scale_type=SpectralFilterBankScaleType.LINEAR) + >>> spec_arr = bft_obj.bft(audio_arr) + >>> spec_arr = np.abs(spec_arr) + + Create Spectral object and extract rms feature + + >>> spectral_obj = af.Spectral(num=bft_obj.num, + >>> fre_band_arr=bft_obj.get_fre_band_arr()) + >>> n_time = spec_arr.shape[1] # Or use bft_obj.cal_time_length(len(audio_arr)) + >>> spectral_obj.set_time_length(n_time) + >>> rms_arr = spectral_obj.rms(spec_arr) + + Display plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_plot, fill_wave + >>> fig, ax = plt.subplots(nrows=2, sharex=True) + >>> fill_wave(audio_arr, samplate=sr, axes=ax[0]) + >>> times = np.arange(0, len(rms_arr)) * (bft_obj.slide_length / bft_obj.samplate) + >>> fill_plot(times, rms_arr, axes=ax[1], label='rms') + """ + m_data_arr = ascontiguous_T(m_data_arr, dtype=np.float32) + + fn = self._lib['spectralObj_rms'] + fn.argtypes = [ + POINTER(OpaqueSpectral), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS') + ] + n_len, m_len = m_data_arr.shape # t * n + ret = np.zeros(n_len, dtype=np.float32) + fn(self._obj, m_data_arr, ret) + return ret + + def energy(self, m_data_arr, is_log=False, gamma=10.): + """ + Compute the spectral energy feature. + + :math:`\qquad energy=\sum_{n=1}^N x^2[n] =\\frac{1}{N}\sum_{m=1}^N |X[m]|^2` + + Parameters + ---------- + m_data_arr: np.ndarray [shape=(fre, time)] + Spectrogram data + + is_log: bool + Whether to log + + gamma: float + energy gamma value. + + Returns + ------- + energy: np.ndarray [shape=(time,)] + energy frequency for each time + + Examples + -------- + + Read chord audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('chord') + >>> audio_arr, sr = af.read(audio_path) + + Create BFT-Linear object and extract spectrogram + + >>> from audioflux.type import SpectralFilterBankScaleType, SpectralDataType + >>> import numpy as np + >>> bft_obj = af.BFT(num=2049, samplate=sr, radix2_exp=12, slide_length=1024, + >>> data_type=SpectralDataType.MAG, + >>> scale_type=SpectralFilterBankScaleType.LINEAR) + >>> spec_arr = bft_obj.bft(audio_arr) + >>> spec_arr = np.abs(spec_arr) + + Create Spectral object and extract energy feature + + >>> spectral_obj = af.Spectral(num=bft_obj.num, + >>> fre_band_arr=bft_obj.get_fre_band_arr()) + >>> n_time = spec_arr.shape[1] # Or use bft_obj.cal_time_length(len(audio_arr)) + >>> spectral_obj.set_time_length(n_time) + >>> energy_arr = spectral_obj.energy(spec_arr) + + Display plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_plot, fill_wave + >>> fig, ax = plt.subplots(nrows=2, sharex=True) + >>> fill_wave(audio_arr, samplate=sr, axes=ax[0]) + >>> times = np.arange(0, len(energy_arr)) * (bft_obj.slide_length / bft_obj.samplate) + >>> fill_plot(times, energy_arr, axes=ax[1], label='energy') + """ + m_data_arr = ascontiguous_T(m_data_arr, dtype=np.float32) + + fn = self._lib['spectralObj_energy'] + fn.argtypes = [ + POINTER(OpaqueSpectral), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + c_int, + c_float, + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS') + ] + n_len, m_len = m_data_arr.shape # t * n + ret = np.zeros(n_len, dtype=np.float32) + fn(self._obj, m_data_arr, int(is_log), gamma, ret) + return ret + + def hfc(self, m_data_arr): + """ + Compute the spectral hfc feature. + + :math:`\qquad hfc(t)=\\frac{\sum_{k=b_1}^{b_2} s_k(t)k }{b_2-b_1+1}` + + * :math:`b_1` and :math:`b_2`: the frequency band bin boundaries + * :math:`s_k`: the spectrum value, which can be magnitude spectrum or power spectrum + + Parameters + ---------- + m_data_arr: np.ndarray [shape=(fre, time)] + Spectrogram data + + Returns + ------- + hfc: np.ndarray [shape=(time,)] + hfc frequency for each time + + Examples + -------- + + Read chord audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('chord') + >>> audio_arr, sr = af.read(audio_path) + + Create BFT-Linear object and extract spectrogram + + >>> from audioflux.type import SpectralFilterBankScaleType, SpectralDataType + >>> import numpy as np + >>> bft_obj = af.BFT(num=2049, samplate=sr, radix2_exp=12, slide_length=1024, + >>> data_type=SpectralDataType.MAG, + >>> scale_type=SpectralFilterBankScaleType.LINEAR) + >>> spec_arr = bft_obj.bft(audio_arr) + >>> spec_arr = np.abs(spec_arr) + + Create Spectral object and extract hfc feature + + >>> spectral_obj = af.Spectral(num=bft_obj.num, + >>> fre_band_arr=bft_obj.get_fre_band_arr()) + >>> n_time = spec_arr.shape[1] # Or use bft_obj.cal_time_length(len(audio_arr)) + >>> spectral_obj.set_time_length(n_time) + >>> hfc_arr = spectral_obj.hfc(spec_arr) + + Display plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_plot, fill_wave + >>> fig, ax = plt.subplots(nrows=2, sharex=True) + >>> fill_wave(audio_arr, samplate=sr, axes=ax[0]) + >>> times = np.arange(0, len(hfc_arr)) * (bft_obj.slide_length / bft_obj.samplate) + >>> fill_plot(times, hfc_arr, axes=ax[1], label='hfc') + """ + m_data_arr = ascontiguous_T(m_data_arr, dtype=np.float32) + + fn = self._lib['spectralObj_hfc'] + fn.argtypes = [ + POINTER(OpaqueSpectral), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS') + ] + n_len, m_len = m_data_arr.shape # t * n + ret = np.zeros(n_len, dtype=np.float32) + fn(self._obj, m_data_arr, ret) + return ret + + def sd(self, m_data_arr, step=1, is_positive=False): + """ + Compute the spectral sd feature. + + :math:`\qquad sd(t)=flux(t)` + + satisfies the calculation of :math:`s_k(t) \ge s_k(t-1)`, :math:`p=2`,the result is not :math:`1/p` + + * :math:`s_k`: the spectrum value, which can be magnitude spectrum or power spectrum + * flux: `Spectral.flux` + + Parameters + ---------- + m_data_arr: np.ndarray [shape=(fre, time)] + Spectrogram data + + step: int + Compute time axis steps, like 1/2/3/... + + is_positive: bool + Whether to set negative numbers to 0 + + Returns + ------- + sd: np.ndarray [shape=(time,)] + sd frequency for each time + + Examples + -------- + + Read chord audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('chord') + >>> audio_arr, sr = af.read(audio_path) + + Create BFT-Linear object and extract spectrogram + + >>> from audioflux.type import SpectralFilterBankScaleType, SpectralDataType + >>> import numpy as np + >>> bft_obj = af.BFT(num=2049, samplate=sr, radix2_exp=12, slide_length=1024, + >>> data_type=SpectralDataType.MAG, + >>> scale_type=SpectralFilterBankScaleType.LINEAR) + >>> spec_arr = bft_obj.bft(audio_arr) + >>> spec_arr = np.abs(spec_arr) + + Create Spectral object and extract sd feature + + >>> spectral_obj = af.Spectral(num=bft_obj.num, + >>> fre_band_arr=bft_obj.get_fre_band_arr()) + >>> n_time = spec_arr.shape[1] # Or use bft_obj.cal_time_length(len(audio_arr)) + >>> spectral_obj.set_time_length(n_time) + >>> sd_arr = spectral_obj.sd(spec_arr) + + Display plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_plot, fill_wave + >>> fig, ax = plt.subplots(nrows=2, sharex=True) + >>> fill_wave(audio_arr, samplate=sr, axes=ax[0]) + >>> times = np.arange(0, len(sd_arr)) * (bft_obj.slide_length / bft_obj.samplate) + >>> fill_plot(times, sd_arr, axes=ax[1], label='sd') + """ + m_data_arr = ascontiguous_T(m_data_arr, dtype=np.float32) + + fn = self._lib['spectralObj_sd'] + fn.argtypes = [ + POINTER(OpaqueSpectral), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + c_int, + c_int, + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS') + ] + n_len, m_len = m_data_arr.shape # t * n + ret = np.zeros(n_len, dtype=np.float32) + fn(self._obj, m_data_arr, c_int(step), c_int(int(is_positive)), ret) + return ret + + def sf(self, m_data_arr, step=1, is_positive=False): + """ + Compute the spectral sf feature. + + :math:`\qquad sf(t)=flux(t)` + + satisfies the calculation of :math:`s_k(t) \ge s_k(t-1)`, :math:`p=1` + + * :math:`s_k`: the spectrum value, which can be magnitude spectrum or power spectrum + * flux: `Spectral.flux` + + Parameters + ---------- + m_data_arr: np.ndarray [shape=(fre, time)] + Spectrogram data + + step: int + Compute time axis steps, like 1/2/3/... + + is_positive: bool + Whether to set negative numbers to 0 + + Returns + ------- + sf: np.ndarray [shape=(time,)] + sf frequency for each time + + Examples + -------- + + Read chord audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('chord') + >>> audio_arr, sr = af.read(audio_path) + + Create BFT-Linear object and extract spectrogram + + >>> from audioflux.type import SpectralFilterBankScaleType, SpectralDataType + >>> import numpy as np + >>> bft_obj = af.BFT(num=2049, samplate=sr, radix2_exp=12, slide_length=1024, + >>> data_type=SpectralDataType.MAG, + >>> scale_type=SpectralFilterBankScaleType.LINEAR) + >>> spec_arr = bft_obj.bft(audio_arr) + >>> spec_arr = np.abs(spec_arr) + + Create Spectral object and extract sf feature + + >>> spectral_obj = af.Spectral(num=bft_obj.num, + >>> fre_band_arr=bft_obj.get_fre_band_arr()) + >>> n_time = spec_arr.shape[1] # Or use bft_obj.cal_time_length(len(audio_arr)) + >>> spectral_obj.set_time_length(n_time) + >>> sf_arr = spectral_obj.sf(spec_arr) + + Display plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_plot, fill_wave + >>> fig, ax = plt.subplots(nrows=2, sharex=True) + >>> fill_wave(audio_arr, samplate=sr, axes=ax[0]) + >>> times = np.arange(0, len(sf_arr)) * (bft_obj.slide_length / bft_obj.samplate) + >>> fill_plot(times, sf_arr, axes=ax[1], label='sf') + """ + m_data_arr = ascontiguous_T(m_data_arr, dtype=np.float32) + + fn = self._lib['spectralObj_sf'] + fn.argtypes = [ + POINTER(OpaqueSpectral), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + c_int, + c_int, + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS') + ] + n_len, m_len = m_data_arr.shape # t * n + ret = np.zeros(n_len, dtype=np.float32) + fn(self._obj, m_data_arr, c_int(step), c_int(int(is_positive)), ret) + return ret + + def mkl(self, m_data_arr, tp=0): + """ + Compute the spectral mkl feature. + + :math:`\qquad mkl(t)=\sum_{k=b_1}^{b_2} \log\\left(1+ \cfrac {s_k(t)}{s_k(t-1)} \\right)` + + * :math:`b_1` and :math:`b_2`: the frequency band bin boundaries + * :math:`s_k`: the spectrum value, which can be magnitude spectrum or power spectrum + + Parameters + ---------- + m_data_arr: np.ndarray [shape=(fre, time)] + Spectrogram data + + tp: int, 0 or 1 + 0 sum 1 mean + + Returns + ------- + mkl: np.ndarray [shape=(time,)] + mkl frequency for each time + + Examples + -------- + + Read chord audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('chord') + >>> audio_arr, sr = af.read(audio_path) + + Create BFT-Linear object and extract spectrogram + + >>> from audioflux.type import SpectralFilterBankScaleType, SpectralDataType + >>> import numpy as np + >>> bft_obj = af.BFT(num=2049, samplate=sr, radix2_exp=12, slide_length=1024, + >>> data_type=SpectralDataType.MAG, + >>> scale_type=SpectralFilterBankScaleType.LINEAR) + >>> spec_arr = bft_obj.bft(audio_arr) + >>> spec_arr = np.abs(spec_arr) + + Create Spectral object and extract mkl feature + + >>> spectral_obj = af.Spectral(num=bft_obj.num, + >>> fre_band_arr=bft_obj.get_fre_band_arr()) + >>> n_time = spec_arr.shape[1] # Or use bft_obj.cal_time_length(len(audio_arr)) + >>> spectral_obj.set_time_length(n_time) + >>> mkl_arr = spectral_obj.mkl(spec_arr) + + Display plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_plot, fill_wave + >>> fig, ax = plt.subplots(nrows=2, sharex=True) + >>> fill_wave(audio_arr, samplate=sr, axes=ax[0]) + >>> times = np.arange(0, len(mkl_arr)) * (bft_obj.slide_length / bft_obj.samplate) + >>> fill_plot(times, mkl_arr, axes=ax[1], label='mkl') + """ + m_data_arr = ascontiguous_T(m_data_arr, dtype=np.float32) + + fn = self._lib['spectralObj_mkl'] + fn.argtypes = [ + POINTER(OpaqueSpectral), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + c_int, + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS') + ] + n_len, m_len = m_data_arr.shape # t * n + ret = np.zeros(n_len, dtype=np.float32) + fn(self._obj, m_data_arr, c_int(tp), ret) + return ret + + def pd(self, m_data_arr, m_phase_arr): + """ + Compute the spectral pd feature. + + :math:`\qquad \psi_k(t)` is set as the phase function of point `k` at time `t`. + + :math:`\qquad \psi_k^{\prime}(t)=\psi_k(t)-\psi_k(t-1)` + + :math:`\qquad \psi_k^{\prime\prime}(t)=\psi_k^{\prime}(t)-\psi_k^{\prime}(t-1) = \psi_k(t)-2\psi_k(t-1)+\psi_k(t-2)` + + :math:`\qquad pd(t)= \\frac {\sum_{k=b_1}^{b_2} \| \psi_k^{\prime\prime}(t) \|} {b_2-b_1+1}` + + * :math:`b_1` and :math:`b_2`: the frequency band bin boundaries + + Parameters + ---------- + m_data_arr: np.ndarray [shape=(fre, time)] + Spectrogram data + + m_phase_arr: np.ndarray [shape=(fre, time)] + Phase data + + Returns + ------- + pd: np.ndarray [shape=(time,)] + pd frequency for each time + + Examples + -------- + + Read chord audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('chord') + >>> audio_arr, sr = af.read(audio_path) + + Create BFT-Linear object and extract spectrogram + + >>> from audioflux.type import SpectralFilterBankScaleType, SpectralDataType + >>> import numpy as np + >>> bft_obj = af.BFT(num=2049, samplate=sr, radix2_exp=12, slide_length=1024, + >>> data_type=SpectralDataType.MAG, + >>> scale_type=SpectralFilterBankScaleType.LINEAR) + >>> spec_arr = bft_obj.bft(audio_arr) + >>> phase_arr = af.utils.get_phase(spec_arr) + >>> spec_arr = np.abs(spec_arr) + + Create Spectral object and extract pd feature + + >>> spectral_obj = af.Spectral(num=bft_obj.num, + >>> fre_band_arr=bft_obj.get_fre_band_arr()) + >>> n_time = spec_arr.shape[1] # Or use bft_obj.cal_time_length(len(audio_arr)) + >>> spectral_obj.set_time_length(n_time) + >>> pd_arr = spectral_obj.pd(spec_arr, phase_arr) + """ + m_data_arr = ascontiguous_T(m_data_arr, dtype=np.float32) + m_phase_arr = ascontiguous_T(m_phase_arr) + + fn = self._lib['spectralObj_pd'] + fn.argtypes = [ + POINTER(OpaqueSpectral), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS') + ] + n_len, m_len = m_data_arr.shape # t * n + ret = np.zeros(n_len, dtype=np.float32) + fn(self._obj, m_data_arr, m_phase_arr, ret) + return ret + + def wpd(self, m_data_arr, m_phase_arr): + """ + Compute the spectral wpd feature. + + :math:`\qquad \psi_k(t)` is set as the phase function of point k at time t. + + :math:`\qquad \psi_k^{\prime}(t)=\psi_k(t)-\psi_k(t-1)` + + :math:`\qquad \psi_k^{\prime\prime}(t)=\psi_k^{\prime}(t)-\psi_k^{\prime}(t-1) = \psi_k(t)-2\psi_k(t-1)+\psi_k(t-2)` + + :math:`\qquad wpd(t)= \\frac {\sum_{k=b_1}^{b_2} \| \psi_k^{\prime\prime}(t) \|s_k(t)}{b_2-b_1+1}` + + * :math:`b_1` and :math:`b_2`: the frequency band bin boundaries + * :math:`s_k`: the spectrum value, which can be magnitude spectrum or power spectrum + + Parameters + ---------- + m_data_arr: np.ndarray [shape=(fre, time)] + Spectrogram data + + m_phase_arr: np.ndarray [shape=(fre, time)] + Phase data + + Returns + ------- + wpd: np.ndarray [shape=(time,)] + wpd frequency for each time + + Examples + -------- + + Read chord audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('chord') + >>> audio_arr, sr = af.read(audio_path) + + Create BFT-Linear object and extract spectrogram + + >>> from audioflux.type import SpectralFilterBankScaleType, SpectralDataType + >>> import numpy as np + >>> bft_obj = af.BFT(num=2049, samplate=sr, radix2_exp=12, slide_length=1024, + >>> data_type=SpectralDataType.MAG, + >>> scale_type=SpectralFilterBankScaleType.LINEAR) + >>> spec_arr = bft_obj.bft(audio_arr) + >>> phase_arr = af.utils.get_phase(spec_arr) + >>> spec_arr = np.abs(spec_arr) + + Create Spectral object and extract wpd feature + + >>> spectral_obj = af.Spectral(num=bft_obj.num, + >>> fre_band_arr=bft_obj.get_fre_band_arr()) + >>> n_time = spec_arr.shape[1] # Or use bft_obj.cal_time_length(len(audio_arr)) + >>> spectral_obj.set_time_length(n_time) + >>> wpd_arr = spectral_obj.wpd(spec_arr, phase_arr) + + Display plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_plot, fill_wave + >>> fig, ax = plt.subplots(nrows=2, sharex=True) + >>> fill_wave(audio_arr, samplate=sr, axes=ax[0]) + >>> times = np.arange(0, len(wpd_arr)) * (bft_obj.slide_length / bft_obj.samplate) + >>> fill_plot(times, wpd_arr, axes=ax[1], label='wpd') + """ + m_data_arr = ascontiguous_T(m_data_arr, dtype=np.float32) + m_phase_arr = ascontiguous_T(m_phase_arr) + + fn = self._lib['spectralObj_wpd'] + fn.argtypes = [ + POINTER(OpaqueSpectral), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS') + ] + n_len, m_len = m_data_arr.shape # t * n + ret = np.zeros(n_len, dtype=np.float32) + fn(self._obj, m_data_arr, m_phase_arr, ret) + return ret + + def nwpd(self, m_data_arr, m_phase_arr): + """ + Compute the spectral nwpd feature. + + :math:`\qquad nwpd(t)= \\frac {wpd} {\mu_s}` + + * wpd: `Spectral.wpd` + * :math:`\mu_s`: the mean of :math:`s_k(t)` + * :math:`s_k`: the spectrum value, which can be magnitude spectrum or power spectrum + + Parameters + ---------- + m_data_arr: np.ndarray [shape=(fre, time)] + Spectrogram data + + m_phase_arr: np.ndarray [shape=(fre, time)] + Phase data + + Returns + ------- + nwpd: np.ndarray [shape=(time,)] + nwpd frequency for each time + + Examples + -------- + + Read chord audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('chord') + >>> audio_arr, sr = af.read(audio_path) + + Create BFT-Linear object and extract spectrogram + + >>> from audioflux.type import SpectralFilterBankScaleType, SpectralDataType + >>> import numpy as np + >>> bft_obj = af.BFT(num=2049, samplate=sr, radix2_exp=12, slide_length=1024, + >>> data_type=SpectralDataType.MAG, + >>> scale_type=SpectralFilterBankScaleType.LINEAR) + >>> spec_arr = bft_obj.bft(audio_arr) + >>> phase_arr = af.utils.get_phase(spec_arr) + >>> spec_arr = np.abs(spec_arr) + + Create Spectral object and extract nwpd feature + + >>> spectral_obj = af.Spectral(num=bft_obj.num, + >>> fre_band_arr=bft_obj.get_fre_band_arr()) + >>> n_time = spec_arr.shape[1] # Or use bft_obj.cal_time_length(len(audio_arr)) + >>> spectral_obj.set_time_length(n_time) + >>> nwpd_arr = spectral_obj.nwpd(spec_arr, phase_arr) + """ + m_data_arr = ascontiguous_T(m_data_arr, dtype=np.float32) + m_phase_arr = ascontiguous_T(m_phase_arr) + + fn = self._lib['spectralObj_nwpd'] + fn.argtypes = [ + POINTER(OpaqueSpectral), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS') + ] + n_len, m_len = m_data_arr.shape # t * n + ret = np.zeros(n_len, dtype=np.float32) + fn(self._obj, m_data_arr, m_phase_arr, ret) + return ret + + def cd(self, m_data_arr, m_phase_arr): + """ + Compute the spectral cd feature. + + :math:`\qquad \psi_k(t)` is set as the phase function of point k at time t. + + :math:`\qquad \\alpha_k(t)=s_k(t) e^{j(2\psi_k(t)-\psi_k(t-1))}` + + :math:`\qquad \\beta_k(t)=s_k(t) e^{j\psi_k(t)}` + + :math:`\qquad cd(t)=\sum_{k=b_1}^{b_2} \| \\beta_k(t)-\\alpha_k(t-1) \|` + + * :math:`b_1` and :math:`b_2`: the frequency band bin boundaries + * :math:`s_k`: the spectrum value, which can be magnitude spectrum or power spectrum + + Parameters + ---------- + m_data_arr: np.ndarray [shape=(fre, time)] + Spectrogram data + + m_phase_arr: np.ndarray [shape=(fre, time)] + Phase data + + Returns + ------- + cd: np.ndarray [shape=(time,)] + cd frequency for each time + + Examples + -------- + + Read chord audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('chord') + >>> audio_arr, sr = af.read(audio_path) + + Create BFT-Linear object and extract spectrogram + + >>> from audioflux.type import SpectralFilterBankScaleType, SpectralDataType + >>> import numpy as np + >>> bft_obj = af.BFT(num=2049, samplate=sr, radix2_exp=12, slide_length=1024, + >>> data_type=SpectralDataType.MAG, + >>> scale_type=SpectralFilterBankScaleType.LINEAR) + >>> spec_arr = bft_obj.bft(audio_arr) + >>> phase_arr = af.utils.get_phase(spec_arr) + >>> spec_arr = np.abs(spec_arr) + + Create Spectral object and extract cd feature + + >>> spectral_obj = af.Spectral(num=bft_obj.num, + >>> fre_band_arr=bft_obj.get_fre_band_arr()) + >>> n_time = spec_arr.shape[1] # Or use bft_obj.cal_time_length(len(audio_arr)) + >>> spectral_obj.set_time_length(n_time) + >>> cd_arr = spectral_obj.cd(spec_arr, phase_arr) + + Display plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_plot, fill_wave + >>> fig, ax = plt.subplots(nrows=2, sharex=True) + >>> fill_wave(audio_arr, samplate=sr, axes=ax[0]) + >>> times = np.arange(0, len(cd_arr)) * (bft_obj.slide_length / bft_obj.samplate) + >>> fill_plot(times, cd_arr, axes=ax[1], label='cd') + """ + m_data_arr = ascontiguous_T(m_data_arr, dtype=np.float32) + m_phase_arr = ascontiguous_T(m_phase_arr) + + fn = self._lib['spectralObj_cd'] + fn.argtypes = [ + POINTER(OpaqueSpectral), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS') + ] + n_len, m_len = m_data_arr.shape # t * n + ret = np.zeros(n_len, dtype=np.float32) + fn(self._obj, m_data_arr, m_phase_arr, ret) + return ret + + def rcd(self, m_data_arr, m_phase_arr): + """ + Compute the spectral rcd feature. + + :math:`\qquad rcd(t)=cd` + + participate in the sum calculation when :math:`s_k(t) \geq s_k(t-1)` is satisfied + + * cd: `Spectral.cd` + * :math:`s_k`: the spectrum value, which can be magnitude spectrum or power spectrum + + Parameters + ---------- + m_data_arr: np.ndarray [shape=(fre, time)] + Spectrogram data + + m_phase_arr: np.ndarray [shape=(fre, time)] + Phase data + + Returns + ------- + rcd: np.ndarray [shape=(time,)] + rcd frequency for each time + + Examples + -------- + + Read chord audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('chord') + >>> audio_arr, sr = af.read(audio_path) + + Create BFT-Linear object and extract spectrogram + + >>> from audioflux.type import SpectralFilterBankScaleType, SpectralDataType + >>> import numpy as np + >>> bft_obj = af.BFT(num=2049, samplate=sr, radix2_exp=12, slide_length=1024, + >>> data_type=SpectralDataType.MAG, + >>> scale_type=SpectralFilterBankScaleType.LINEAR) + >>> spec_arr = bft_obj.bft(audio_arr) + >>> phase_arr = af.utils.get_phase(spec_arr) + >>> spec_arr = np.abs(spec_arr) + + Create Spectral object and extract rcd feature + + >>> spectral_obj = af.Spectral(num=bft_obj.num, + >>> fre_band_arr=bft_obj.get_fre_band_arr()) + >>> n_time = spec_arr.shape[1] # Or use bft_obj.cal_time_length(len(audio_arr)) + >>> spectral_obj.set_time_length(n_time) + >>> rcd_arr = spectral_obj.rcd(spec_arr, phase_arr) + + Display plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_plot, fill_wave + >>> fig, ax = plt.subplots(nrows=2, sharex=True) + >>> fill_wave(audio_arr, samplate=sr, axes=ax[0]) + >>> times = np.arange(0, len(rcd_arr)) * (bft_obj.slide_length / bft_obj.samplate) + >>> fill_plot(times, rcd_arr, axes=ax[1], label='rcd') + """ + m_data_arr = ascontiguous_T(m_data_arr, dtype=np.float32) + m_phase_arr = ascontiguous_T(m_phase_arr) + + fn = self._lib['spectralObj_rcd'] + fn.argtypes = [ + POINTER(OpaqueSpectral), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS') + ] + n_len, m_len = m_data_arr.shape # t * n + ret = np.zeros(n_len, dtype=np.float32) + fn(self._obj, m_data_arr, m_phase_arr, ret) + return ret + + def broadband(self, m_data_arr, threshold=0): + """ + Compute the spectral broadband feature. + + Parameters + ---------- + m_data_arr: np.ndarray [shape=(fre, time)] + Spectrogram data + + threshold: float, [0,1] + broadband threshold + + Returns + ------- + broadband: np.ndarray [shape=(time,)] + broadband frequency for each time + + Examples + -------- + + Read chord audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('chord') + >>> audio_arr, sr = af.read(audio_path) + + Create BFT-Linear object and extract spectrogram + + >>> from audioflux.type import SpectralFilterBankScaleType, SpectralDataType + >>> import numpy as np + >>> bft_obj = af.BFT(num=2049, samplate=sr, radix2_exp=12, slide_length=1024, + >>> data_type=SpectralDataType.MAG, + >>> scale_type=SpectralFilterBankScaleType.LINEAR) + >>> spec_arr = bft_obj.bft(audio_arr) + >>> spec_arr = np.abs(spec_arr) + + Create Spectral object and extract broadband feature + + >>> spectral_obj = af.Spectral(num=bft_obj.num, + >>> fre_band_arr=bft_obj.get_fre_band_arr()) + >>> n_time = spec_arr.shape[1] # Or use bft_obj.cal_time_length(len(audio_arr)) + >>> spectral_obj.set_time_length(n_time) + >>> broadband_arr = spectral_obj.broadband(spec_arr) + + Display plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_plot, fill_wave + >>> fig, ax = plt.subplots(nrows=2, sharex=True) + >>> fill_wave(audio_arr, samplate=sr, axes=ax[0]) + >>> times = np.arange(0, len(broadband_arr)) * (bft_obj.slide_length / bft_obj.samplate) + >>> fill_plot(times, broadband_arr, axes=ax[1], label='broadband') + """ + if not 0 <= threshold <= 1: + raise ValueError(f'threshold must be 0 or 1') + + m_data_arr = ascontiguous_T(m_data_arr, dtype=np.float32) + + fn = self._lib['spectralObj_broadband'] + fn.argtypes = [ + POINTER(OpaqueSpectral), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + c_float, + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS') + ] + n_len, m_len = m_data_arr.shape # t * n + ret_arr = np.zeros(n_len, dtype=np.float32) + fn(self._obj, m_data_arr, c_float(threshold), ret_arr) + return ret_arr + + def novelty(self, m_data_arr, step=1, threshold=0., + method_type=SpectralNoveltyMethodType.SUB, + data_type=SpectralNoveltyDataType.VALUE): + """ + Compute the spectral novelty feature. + + Parameters + ---------- + m_data_arr: np.ndarray [shape=(fre, time)] + Spectrogram data + + step: int + Compute time axis steps, like 1/2/3/... + + threshold: float [0,1] + Novelty threshold. + + method_type: SpectralNoveltyMethodType + Novelty method type. + + data_type: SpectralNoveltyDataType + Novelty data type. + + Returns + ------- + novelty: np.ndarray [shape=(time,)] + Novelty frequency per time step. + + Examples + -------- + + Read chord audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('chord') + >>> audio_arr, sr = af.read(audio_path) + + Create BFT-Linear object and extract spectrogram + + >>> from audioflux.type import SpectralFilterBankScaleType, SpectralDataType + >>> import numpy as np + >>> bft_obj = af.BFT(num=2049, samplate=sr, radix2_exp=12, slide_length=1024, + >>> data_type=SpectralDataType.MAG, + >>> scale_type=SpectralFilterBankScaleType.LINEAR) + >>> spec_arr = bft_obj.bft(audio_arr) + >>> spec_arr = np.abs(spec_arr) + + Create Spectral object and extract novelty feature + + >>> spectral_obj = af.Spectral(num=bft_obj.num, + >>> fre_band_arr=bft_obj.get_fre_band_arr()) + >>> n_time = spec_arr.shape[1] # Or use bft_obj.cal_time_length(len(audio_arr)) + >>> spectral_obj.set_time_length(n_time) + >>> novelty_arr = spectral_obj.novelty(spec_arr) + + Display plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_plot, fill_wave + >>> fig, ax = plt.subplots(nrows=2, sharex=True) + >>> fill_wave(audio_arr, samplate=sr, axes=ax[0]) + >>> times = np.arange(0, len(novelty_arr)) * (bft_obj.slide_length / bft_obj.samplate) + >>> fill_plot(times, novelty_arr, axes=ax[1], label='novelty') + """ + m_data_arr = ascontiguous_T(m_data_arr, dtype=np.float32) + + fn = self._lib['spectralObj_novelty'] + fn.argtypes = [ + POINTER(OpaqueSpectral), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + c_int, + c_float, + POINTER(c_int), + POINTER(c_int), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS'), + ] + n_len, m_len = m_data_arr.shape # t * n + ret_arr = np.zeros(n_len, dtype=np.float32) + fn(self._obj, m_data_arr, c_int(step), c_float(threshold), + pointer(c_int(method_type.value)), + pointer(c_int(data_type.value)), + ret_arr) + return ret_arr + + def eef(self, m_data_arr, is_norm=False): + """ + Compute the spectral eef feature. + + :math:`\qquad p_k=\\frac{s_k}{\sum_{k=b_1}^{b_2}s_k}` + + :math:`\qquad entropy2= {-\sum_{ k=b_1 }^{b_2} p_k \log(p_k)}` + + :math:`\qquad eef=\sqrt{ 1+| energy\\times entropy2| }` + + * energy: `Spectral.energy` + * :math:`b_1` and :math:`b_2`: the frequency band bin boundaries + * :math:`s_k`: the spectrum value, which can be magnitude spectrum or power spectrum + + Parameters + ---------- + m_data_arr: np.ndarray [shape=(fre, time)] + Spectrogram data + + is_norm: bool + Whether to norm + + Returns + ------- + eef: np.ndarray [shape=(time,)] + eef frequency for each time + + Examples + -------- + + Read chord audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('chord') + >>> audio_arr, sr = af.read(audio_path) + + Create BFT-Linear object and extract spectrogram + + >>> from audioflux.type import SpectralFilterBankScaleType, SpectralDataType + >>> import numpy as np + >>> bft_obj = af.BFT(num=2049, samplate=sr, radix2_exp=12, slide_length=1024, + >>> data_type=SpectralDataType.MAG, + >>> scale_type=SpectralFilterBankScaleType.LINEAR) + >>> spec_arr = bft_obj.bft(audio_arr) + >>> spec_arr = np.abs(spec_arr) + + Create Spectral object and extract eef feature + + >>> spectral_obj = af.Spectral(num=bft_obj.num, + >>> fre_band_arr=bft_obj.get_fre_band_arr()) + >>> n_time = spec_arr.shape[1] # Or use bft_obj.cal_time_length(len(audio_arr)) + >>> spectral_obj.set_time_length(n_time) + >>> eef_arr = spectral_obj.eef(spec_arr) + + Display plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_plot, fill_wave + >>> fig, ax = plt.subplots(nrows=2, sharex=True) + >>> fill_wave(audio_arr, samplate=sr, axes=ax[0]) + >>> times = np.arange(0, len(eef_arr)) * (bft_obj.slide_length / bft_obj.samplate) + >>> fill_plot(times, eef_arr, axes=ax[1], label='eef') + """ + m_data_arr = ascontiguous_T(m_data_arr, dtype=np.float32) + + fn = self._lib['spectralObj_eef'] + fn.argtypes = [ + POINTER(OpaqueSpectral), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + c_int, + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS'), + ] + n_len, m_len = m_data_arr.shape # t * n + ret_arr = np.zeros(n_len, dtype=np.float32) + fn(self._obj, m_data_arr, c_int(int(is_norm)), ret_arr) + return ret_arr + + def eer(self, m_data_arr, is_norm=False, gamma=1.): + """ + Compute the spectral eer feature. + + :math:`\qquad le=\log_{10}(1+\gamma \\times energy), \gamma \in (0,\infty)`, represents `log` compression of data + + :math:`\qquad p_k=\\frac{s_k}{\sum_{k=b_1}^{b_2}s_k}` + + :math:`\qquad entropy2= {-\sum_{ k=b_1 }^{b_2} p_k \log(p_k)}` + + :math:`\qquad eer=\\sqrt{ 1+\\left| \\cfrac{le}{entropy2}\\right| }` + + * energy: Spectral.energy + * :math:`b_1` and :math:`b_2`: the frequency band bin boundaries + * :math:`s_k`: the spectrum value, which can be magnitude spectrum or power spectrum + + Parameters + ---------- + m_data_arr: np.ndarray [shape=(fre, time)] + Spectrogram data + + is_norm: bool + Whether to norm + + gamma: float + Usually set is 1./10./20.etc, song is 0.5 + + Returns + ------- + eer: np.ndarray [shape=(time,)] + eer frequency for each time + + Examples + -------- + + Read chord audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('chord') + >>> audio_arr, sr = af.read(audio_path) + + Create BFT-Linear object and extract spectrogram + + >>> from audioflux.type import SpectralFilterBankScaleType, SpectralDataType + >>> import numpy as np + >>> bft_obj = af.BFT(num=2049, samplate=sr, radix2_exp=12, slide_length=1024, + >>> data_type=SpectralDataType.MAG, + >>> scale_type=SpectralFilterBankScaleType.LINEAR) + >>> spec_arr = bft_obj.bft(audio_arr) + >>> spec_arr = np.abs(spec_arr) + + Create Spectral object and extract eer feature + + >>> spectral_obj = af.Spectral(num=bft_obj.num, + >>> fre_band_arr=bft_obj.get_fre_band_arr()) + >>> n_time = spec_arr.shape[1] # Or use bft_obj.cal_time_length(len(audio_arr)) + >>> spectral_obj.set_time_length(n_time) + >>> eer_arr = spectral_obj.eer(spec_arr) + + Display plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_plot, fill_wave + >>> fig, ax = plt.subplots(nrows=2, sharex=True) + >>> fill_wave(audio_arr, samplate=sr, axes=ax[0]) + >>> times = np.arange(0, len(eer_arr)) * (bft_obj.slide_length / bft_obj.samplate) + >>> fill_plot(times, eer_arr, axes=ax[1], label='eer') + """ + m_data_arr = ascontiguous_T(m_data_arr, dtype=np.float32) + + fn = self._lib['spectralObj_eer'] + fn.argtypes = [ + POINTER(OpaqueSpectral), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + c_int, + c_float, + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS'), + ] + n_len, m_len = m_data_arr.shape # t * n + ret_arr = np.zeros(n_len, dtype=np.float32) + fn(self._obj, m_data_arr, c_int(int(is_norm)), c_float(gamma), ret_arr) + return ret_arr + + def max(self, m_data_arr): + """ + Compute the spectral max feature. + + Parameters + ---------- + m_data_arr: np.ndarray [shape=(fre, time)] + Spectrogram data + + Returns + ------- + val_arr: np.ndarray [shape=(time,)] + max value for each time + + fre_arr: np.ndarray [shape=(time,)] + max frequency for each time + + Examples + -------- + + Read chord audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('chord') + >>> audio_arr, sr = af.read(audio_path) + + Create BFT-Linear object and extract spectrogram + + >>> from audioflux.type import SpectralFilterBankScaleType, SpectralDataType + >>> import numpy as np + >>> bft_obj = af.BFT(num=2049, samplate=sr, radix2_exp=12, slide_length=1024, + >>> data_type=SpectralDataType.MAG, + >>> scale_type=SpectralFilterBankScaleType.LINEAR) + >>> spec_arr = bft_obj.bft(audio_arr) + >>> spec_arr = np.abs(spec_arr) + + Create Spectral object and extract max feature + + >>> spectral_obj = af.Spectral(num=bft_obj.num, + >>> fre_band_arr=bft_obj.get_fre_band_arr()) + >>> n_time = spec_arr.shape[1] # Or use bft_obj.cal_time_length(len(audio_arr)) + >>> spectral_obj.set_time_length(n_time) + >>> max_val_arr, max_fre_arr = spectral_obj.max(spec_arr) + + Display plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_plot, fill_wave + >>> fig, ax = plt.subplots(nrows=2, sharex=True) + >>> fill_wave(audio_arr, samplate=sr, axes=ax[0]) + >>> times = np.arange(0, len(max_val_arr)) * (bft_obj.slide_length / bft_obj.samplate) + >>> fill_plot(times, max_val_arr, axes=ax[1], label='max_val') + """ + m_data_arr = ascontiguous_T(m_data_arr, dtype=np.float32) + + fn = self._lib['spectralObj_max'] + fn.argtypes = [ + POINTER(OpaqueSpectral), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS'), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS') + ] + n_len, m_len = m_data_arr.shape # t * n + val_arr = np.zeros(n_len, dtype=np.float32) + fre_arr = np.zeros(n_len, dtype=np.float32) + fn(self._obj, m_data_arr, val_arr, fre_arr) + return val_arr, fre_arr + + def mean(self, m_data_arr): + """ + Compute the spectral mean feature. + + Parameters + ---------- + m_data_arr: np.ndarray [shape=(fre, time)] + Spectrogram data + + Returns + ------- + val_arr: np.ndarray [shape=(time,)] + mean value for each time + + fre_arr: np.ndarray [shape=(time,)] + mean frequency for each time + + Examples + -------- + + Read chord audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('chord') + >>> audio_arr, sr = af.read(audio_path) + + Create BFT-Linear object and extract spectrogram + + >>> from audioflux.type import SpectralFilterBankScaleType, SpectralDataType + >>> import numpy as np + >>> bft_obj = af.BFT(num=2049, samplate=sr, radix2_exp=12, slide_length=1024, + >>> data_type=SpectralDataType.MAG, + >>> scale_type=SpectralFilterBankScaleType.LINEAR) + >>> spec_arr = bft_obj.bft(audio_arr) + >>> spec_arr = np.abs(spec_arr) + + Create Spectral object and extract mean feature + + >>> spectral_obj = af.Spectral(num=bft_obj.num, + >>> fre_band_arr=bft_obj.get_fre_band_arr()) + >>> n_time = spec_arr.shape[1] # Or use bft_obj.cal_time_length(len(audio_arr)) + >>> spectral_obj.set_time_length(n_time) + >>> mean_val_arr, mean_fre_arr = spectral_obj.mean(spec_arr) + + Display plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_plot, fill_wave + >>> fig, ax = plt.subplots(nrows=2, sharex=True) + >>> fill_wave(audio_arr, samplate=sr, axes=ax[0]) + >>> times = np.arange(0, len(mean_val_arr)) * (bft_obj.slide_length / bft_obj.samplate) + >>> fill_plot(times, mean_val_arr, axes=ax[1], label='mean_val') + """ + m_data_arr = ascontiguous_T(m_data_arr, dtype=np.float32) + + fn = self._lib['spectralObj_mean'] + fn.argtypes = [ + POINTER(OpaqueSpectral), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS'), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS') + ] + n_len, m_len = m_data_arr.shape # t * n + val_arr = np.zeros(n_len, dtype=np.float32) + fre_arr = np.zeros(n_len, dtype=np.float32) + fn(self._obj, m_data_arr, val_arr, fre_arr) + return val_arr, fre_arr + + def var(self, m_data_arr): + """ + Compute the spectral var feature. + + Parameters + ---------- + m_data_arr: np.ndarray [shape=(fre, time)] + Spectrogram data + + Returns + ------- + val_arr: np.ndarray [shape=(time,)] + var value for each time + + fre_arr: np.ndarray [shape=(time,)] + var frequency for each time + + Examples + -------- + + Read chord audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('chord') + >>> audio_arr, sr = af.read(audio_path) + + Create BFT-Linear object and extract spectrogram + + >>> from audioflux.type import SpectralFilterBankScaleType, SpectralDataType + >>> import numpy as np + >>> bft_obj = af.BFT(num=2049, samplate=sr, radix2_exp=12, slide_length=1024, + >>> data_type=SpectralDataType.MAG, + >>> scale_type=SpectralFilterBankScaleType.LINEAR) + >>> spec_arr = bft_obj.bft(audio_arr) + >>> spec_arr = np.abs(spec_arr) + + Create Spectral object and extract var feature + + >>> spectral_obj = af.Spectral(num=bft_obj.num, + >>> fre_band_arr=bft_obj.get_fre_band_arr()) + >>> n_time = spec_arr.shape[1] # Or use bft_obj.cal_time_length(len(audio_arr)) + >>> spectral_obj.set_time_length(n_time) + >>> var_val_arr, var_fre_arr = spectral_obj.var(spec_arr) + + Display plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_plot, fill_wave + >>> fig, ax = plt.subplots(nrows=2, sharex=True) + >>> fill_wave(audio_arr, samplate=sr, axes=ax[0]) + >>> times = np.arange(0, len(var_val_arr)) * (bft_obj.slide_length / bft_obj.samplate) + >>> fill_plot(times, var_val_arr, axes=ax[1], label='var_val') + """ + m_data_arr = ascontiguous_T(m_data_arr, dtype=np.float32) + + fn = self._lib['spectralObj_var'] + fn.argtypes = [ + POINTER(OpaqueSpectral), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS'), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS') + ] + n_len, m_len = m_data_arr.shape # t * n + val_arr = np.zeros(n_len, dtype=np.float32) + fre_arr = np.zeros(n_len, dtype=np.float32) + fn(self._obj, m_data_arr, val_arr, fre_arr) + return val_arr, fre_arr + + def __del__(self): + if self._is_created: + fn = self._lib['spectralObj_free'] + fn.argtypes = [POINTER(OpaqueSpectral)] + fn.restype = c_void_p + fn(self._obj) diff --git a/audioflux/feature/xxcc.py b/audioflux/feature/xxcc.py new file mode 100644 index 0000000..da49cfa --- /dev/null +++ b/audioflux/feature/xxcc.py @@ -0,0 +1,204 @@ +import numpy as np +from ctypes import Structure, POINTER, pointer, c_int, c_void_p +from audioflux.type import CepstralRectifyType, CepstralEnergyType +from audioflux.base import Base +from audioflux.utils import ascontiguous_T + +__all__ = ["XXCC"] + + +class OpaqueXXCC(Structure): + _fields_ = [] + + +class XXCC(Base): + """ + Cepstrum coefficients, supports all spectrum types. + + Parameters + ---------- + num: int + Number of frequency bins to generate. It must be the same as the + `num` parameter of the transformation (same as the spectrogram matrix). + + Examples + -------- + + Get a 220Hz's audio file + + >>> import audioflux as af + >>> sample_path = af.utils.sample_path('220') + >>> audio_arr, sr = af.read(sample_path) + + Create BFT object and extract mel spectrogram + + >>> import numpy as np + >>> from audioflux.type import SpectralFilterBankScaleType, SpectralDataType + >>> bft_obj = af.BFT(num=128, radix2_exp=12, samplate=sr, + >>> scale_type=SpectralFilterBankScaleType.MEL, + >>> data_type=SpectralDataType.POWER) + >>> spec_arr = bft_obj.bft(audio_arr) + >>> spec_arr = np.abs(spec_arr) + + Create XXCC object and extract mfcc + + >>> xxcc_obj = af.XXCC(bft_obj.num) + >>> xxcc_obj.set_time_length(time_length=spec_arr.shape[1]) + >>> mfcc_arr = xxcc_obj.xxcc(spec_arr) + + Display MFCC + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_spec + >>> audio_len = audio_arr.shape[0] + >>> fig, ax = plt.subplots() + >>> img = fill_spec(mfcc_arr, axes=ax, + >>> x_coords=bft_obj.x_coords(audio_len), x_axis='time', + >>> title='MFCC') + >>> fig.colorbar(img, ax=ax) + """ + + def __init__(self, num): + super(XXCC, self).__init__(pointer(OpaqueXXCC())) + + self.num = num + + fn = self._lib['xxccObj_new'] + fn.argtypes = [POINTER(POINTER(OpaqueXXCC)), c_int] + fn(self._obj, c_int(self.num)) + self._is_created = True + + def set_time_length(self, time_length): + """ + Set time length + + Parameters + ---------- + time_length: int + """ + + fn = self._lib['xxccObj_setTimeLength'] + fn.argtypes = [ + POINTER(OpaqueXXCC), + c_int + ] + fn(self._obj, c_int(time_length)) + + def xxcc(self, m_data_arr, cc_num=13, rectify_type=CepstralRectifyType.LOG): + """ + Get XX cepstral coefficients. + + Parameters + ---------- + m_data_arr: np.ndarray [shape=(fre, time)] + Spectrogram data + + cc_num: int + xxcc num, usually set to 13, 20 or 40 + + rectify_type: CepstralRectifyType + + Returns + ------- + out: np.ndarray [shape=(cc_num, time)] + """ + if cc_num > self.num: + raise ValueError(f'cc_num={cc_num} must be less than num={self.num}') + m_data_arr = ascontiguous_T(m_data_arr) + if np.iscomplexobj(m_data_arr): + m_data_arr = np.abs(m_data_arr) + + fn = self._lib['xxccObj_xxcc'] + fn.argtypes = [ + POINTER(OpaqueXXCC), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + c_int, + POINTER(c_int), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS') + ] + + time_len, _ = m_data_arr.shape + ret = np.zeros((time_len, cc_num), dtype=np.float32) + fn(self._obj, m_data_arr, c_int(cc_num), pointer(c_int(rectify_type.value)), ret) + return ascontiguous_T(ret) + + def xxcc_standard(self, m_data_arr, energy_arr, cc_num=13, + delta_window_length=9, energy_type=CepstralEnergyType.REPLACE, + rectify_type=CepstralRectifyType.LOG): + """ + Get XX cepstral coefficients standard + + Parameters + ---------- + m_data_arr: np.ndarray [shape=(fre, time)] + Spectrogram data + energy_arr: np.ndarray [shape=(time,] + energy data + cc_num: int + xxcc num, usually set to 13, 20 or 40 + delta_window_length: int + must odd>=3 + energy_type: CepstralEnergyType + rectify_type: CepstralRectifyType + + Returns + ------- + coe_arr: np.ndarray [shape=(cc_num, time) or ()] + m_delta_arr1: np.ndarray [shape=(cc_num, time) or ()] + m_delta_arr2: np.ndarray [shape=(cc_num, time) or ()] + """ + if cc_num > self.num: + raise ValueError(f'cc_num={cc_num} must be less than num={self.num}') + if delta_window_length < 3 or delta_window_length % 2 != 1: + raise ValueError(f'delta_window_length={delta_window_length} must be a strange number greater than 3.') + + m_data_arr = ascontiguous_T(m_data_arr) + if np.iscomplexobj(m_data_arr): + m_data_arr = np.abs(m_data_arr) + + fn = self._lib['xxccObj_xxccStandard'] + fn.argtypes = [ + POINTER(OpaqueXXCC), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + c_int, + np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS'), + POINTER(c_int), + POINTER(c_int), + POINTER(c_int), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS'), + np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='C_CONTIGUOUS') + ] + + time_len, _ = m_data_arr.shape + if energy_type == CepstralEnergyType.APPEND: + shape = (time_len, cc_num + 1) + else: + shape = (time_len, cc_num) + + m_coe_arr = np.zeros(shape, dtype=np.float32) + m_delta_arr1 = np.zeros(shape, dtype=np.float32) + m_delta_arr2 = np.zeros(shape, dtype=np.float32) + + fn(self._obj, + m_data_arr, + c_int(cc_num), + energy_arr, + pointer(c_int(delta_window_length)), + pointer(c_int(energy_type.value)), + pointer(c_int(rectify_type.value)), + m_coe_arr, + m_delta_arr1, + m_delta_arr2 + ) + m_coe_arr = ascontiguous_T(m_coe_arr) + m_delta_arr1 = ascontiguous_T(m_delta_arr1) + m_delta_arr2 = ascontiguous_T(m_delta_arr2) + return m_coe_arr, m_delta_arr1, m_delta_arr2 + + def __del__(self): + if self._is_created: + fn = self._lib['xxccObj_free'] + fn.argtypes = [POINTER(OpaqueXXCC)] + fn.restype = c_void_p + fn(self._obj) diff --git a/audioflux/fst.py b/audioflux/fst.py index 7d9c34d..574a31b 100644 --- a/audioflux/fst.py +++ b/audioflux/fst.py @@ -53,8 +53,6 @@ class FST(Base): >>> # FST can only input fft_length data >>> # For radix2_exp=12, then fft_length=4096 >>> audio_arr = audio_arr[:4096] - array([-5.5879354e-09, -9.3132257e-09, 0.0000000e+00, ..., - -1.3013066e-01, -8.8289484e-02, -7.2360471e-02], dtype=float32) Create FST object @@ -66,20 +64,6 @@ class FST(Base): >>> import numpy as np >>> spec_arr = obj.fst(audio_arr) >>> spec_arr = np.abs(spec_arr) - >>> spec_arr - array([[0.05525468, 0.05525468, 0.05525468, ..., 0.05525468, 0.05525468, - 0.05525468], - [0.13426773, 0.13426773, 0.13426773, ..., 0.13426773, 0.13426773, - 0.13426773], - [0.01777202, 0.01777202, 0.01777202, ..., 0.1509959 , 0.1509959 , - 0.1509959 ], - ..., - [0.02556096, 0.02556096, 0.02556096, ..., 0.00657314, 0.00657314, - 0.00657314], - [0.02556096, 0.02556096, 0.02556096, ..., 0.00657314, 0.00657314, - 0.00657314], - [0.02556096, 0.02556096, 0.02556096, ..., 0.00657314, 0.00657314, - 0.00657314]], dtype=float32) Show spectrogram plot diff --git a/audioflux/mir/harmonic_ratio.py b/audioflux/mir/harmonic_ratio.py index 8c9e431..1d93d8e 100644 --- a/audioflux/mir/harmonic_ratio.py +++ b/audioflux/mir/harmonic_ratio.py @@ -29,6 +29,8 @@ class HarmonicRatio(Base): window_type: WindowType Window type for each frame. + See: `type.WindowType` + slide_length: int Window sliding length. @@ -102,7 +104,7 @@ def harmonic_ratio(self, data_arr): data_len = data_arr.shape[0] time_length = self.cal_time_length(data_len) - # print(time_length) + value_arr = np.zeros(time_length, dtype=np.float32) fn(self._obj, data_arr, c_int(data_len), value_arr) diff --git a/audioflux/mir/hpss.py b/audioflux/mir/hpss.py index 38b1df2..d04a6ea 100644 --- a/audioflux/mir/hpss.py +++ b/audioflux/mir/hpss.py @@ -23,6 +23,8 @@ class HPSS(Base): window_type: WindowType Window type for each frame. + See: `type.WindowType` + slide_length: int Window sliding length. @@ -52,16 +54,17 @@ class HPSS(Base): Disable Plot of Linear spectrogram >>> import numpy as np - >>> from audioflux.type import SpectralFilterBankScaleType + >>> from audioflux.type import SpectralFilterBankScaleType, SpectralDataType >>> bft_obj = af.BFT(num=2049, radix2_exp=12, samplate=sr, - >>> scale_type=SpectralFilterBankScaleType.LINEAR) + >>> scale_type=SpectralFilterBankScaleType.LINEAR, + >>> data_type=SpectralDataType.POWER) >>> audio_arr = audio_arr[:len(h_arr)] - >>> origin_spec_arr = bft_obj.bft(audio_arr) - >>> h_spec_arr = bft_obj.bft(h_arr) - >>> p_spec_arr = bft_obj.bft(p_arr) - >>> origin_spec_arr = np.abs(origin_spec_arr) - >>> h_spec_arr = np.abs(h_spec_arr) - >>> p_spec_arr = np.abs(p_spec_arr) + >>> origin_spec_arr = bft_obj.bft(audio_arr, result_type=1) + >>> h_spec_arr = bft_obj.bft(h_arr, result_type=1) + >>> p_spec_arr = bft_obj.bft(p_arr, result_type=1) + >>> origin_spec_arr = af.utils.power_to_abs_db(origin_spec_arr) + >>> h_spec_arr = af.utils.power_to_abs_db(h_spec_arr) + >>> p_spec_arr = af.utils.power_to_abs_db(p_spec_arr) >>> import matplotlib.pyplot as plt >>> from audioflux.display import fill_spec diff --git a/audioflux/mir/onset.py b/audioflux/mir/onset.py index b943702..d2d8b78 100644 --- a/audioflux/mir/onset.py +++ b/audioflux/mir/onset.py @@ -55,7 +55,7 @@ class Onset(Base): >>> from audioflux.type import SpectralFilterBankScaleType, SpectralDataType >>> import numpy as np - >>> bft_obj = af.BFT(num=128, samplate=sr, radix2_exp=11, slide_length=2048, + >>> bft_obj = af.BFT(num=128, samplate=sr, radix2_exp=12, slide_length=2048, >>> scale_type=SpectralFilterBankScaleType.MEL, >>> data_type=SpectralDataType.POWER) >>> spec_arr = bft_obj.bft(audio_arr) @@ -80,8 +80,6 @@ class Onset(Base): >>> title='Onset') >>> >>> ax = fill_wave(audio_arr, samplate=sr, axes=axes[1]) - >>> ax.vlines(time_arr, -1, 1, color='r', alpha=0.9, - >>> linestyle='--', label='Onsets') >>> >>> times = np.arange(0, len(evn_arr)) * (bft_obj.slide_length / sr) >>> ax = fill_plot(times, evn_arr, axes=axes[2], label='Onset strength') diff --git a/audioflux/mir/pitch.py b/audioflux/mir/pitch.py index 540e83c..7415525 100644 --- a/audioflux/mir/pitch.py +++ b/audioflux/mir/pitch.py @@ -45,25 +45,13 @@ class Pitch(Base): >>> import audioflux as af >>> audio_arr, sr = af.read(af.utils.sample_path('220')) - >>> audio_arr = audio_arr[:8192] + # >>> audio_arr = audio_arr[:8192] Create Pitch object and get frequency >>> from audioflux.type import PitchType >>> obj = af.Pitch(pitch_type=PitchType.YIN) >>> fre_arr, value_arr1, value_arr2 = obj.pitch(audio_arr) - >>> # fre_arr is 220Hz - >>> fre_arr - array([ 0. , 0. , 220.73265, 220.68478, 220.61868], - dtype=float32) - - >>> value_arr1 - array([0. , 0. , 0.03432978, 0.00873703, 0.00305533], - dtype=float32) - - >>> value_arr2 - array([0.26218787, 0.14966235, 0.03079604, 0.00542674, 0.0016437 ], - dtype=float32) Display plot @@ -71,10 +59,11 @@ class Pitch(Base): >>> from audioflux.display import fill_wave, fill_plot >>> import numpy as np >>> audio_len = audio_arr.shape[0] - >>> fig, axes = plt.subplots(nrows=3, sharex=True) + >>> fig, axes = plt.subplots(nrows=2) >>> fill_wave(audio_arr, samplate=sr, axes=axes[0]) >>> - >>> fill_plot(np.arange(len(fre_arr)), fre_arr, label='fre') + >>> ax = fill_plot(np.arange(len(fre_arr)), fre_arr, label='fre', axes=axes[1]) + >>> ax.set_ylabel('frequency(Hz)') """ def __init__(self, pitch_type=None, samplate=32000, diff --git a/audioflux/nsgt.py b/audioflux/nsgt.py index df3451e..daac522 100644 --- a/audioflux/nsgt.py +++ b/audioflux/nsgt.py @@ -86,10 +86,8 @@ class NSGT(Base): >>> audio_path = af.utils.sample_path('220') >>> audio_arr, sr = af.read(audio_path) >>> # NSGT can only input fft_length data - >>> # For radix2_exp=15, then fft_length=2**15= + >>> # For radix2_exp=15, then fft_length=2**15=32768 >>> audio_arr = audio_arr[:32768] - array([-5.5879354e-09, -9.3132257e-09, 0.0000000e+00, ..., - -1.3137090e-01, -1.5649168e-01, -1.8550715e-01], dtype=float32) Create NSGT object of Octave @@ -107,19 +105,6 @@ class NSGT(Base): >>> import numpy as np >>> spec_arr = obj.nsgt(audio_arr) >>> spec_arr = np.abs(spec_arr) - array([[1.6688864e+01, 1.6688864e+01, 1.6688864e+01, ..., 3.6051276e+00, - 3.6051276e+00, 3.6051276e+00], - [1.2269066e+01, 1.2269066e+01, 1.2269066e+01, ..., 2.2744296e+00, - 2.2744296e+00, 2.2744296e+00], - [8.4105768e+00, 8.4105768e+00, 8.4105768e+00, ..., 3.0433545e+00, - 3.0433545e+00, 3.0433545e+00], - ..., - [1.6345095e-02, 1.4781086e-01, 2.2834454e-01, ..., 1.0761657e-02, - 7.4106222e-03, 7.7240127e-03], - [1.3756215e-02, 5.9653565e-02, 1.4187603e-01, ..., 1.1111894e-03, - 5.4555084e-03, 3.7545679e-04], - [6.0960581e-03, 4.1618239e-02, 6.6681899e-02, ..., 1.9094696e-03, - 3.1084872e-03, 2.7320804e-03]], dtype=float32) Show spectrogram plot @@ -144,6 +129,12 @@ def __init__(self, num=84, radix2_exp=12, samplate=32000, normal_type=SpectralFilterBankNormalType.BAND_WIDTH): super(NSGT, self).__init__(pointer(OpaqueNSGT())) + self.fft_length = fft_length = 1 << radix2_exp + + # check num + if num > (fft_length // 2 + 1): + raise ValueError(f'num={num} is too large') + # check BPO if scale_type == SpectralFilterBankScaleType.OCTAVE and bin_per_octave < 1: raise ValueError(f'bin_per_octave={bin_per_octave} must be a positive integer') @@ -174,8 +165,6 @@ def __init__(self, num=84, radix2_exp=12, samplate=32000, # linear/linspace/mel/bark/erb low_fre>=0 raise ValueError(f'{scale_type.name} low_fre={low_fre} must be a non-negative number') - self.fft_length = 1 << radix2_exp - self.num = num self.radix2_exp = radix2_exp self.samplate = samplate diff --git a/audioflux/pwt.py b/audioflux/pwt.py index 8f3dd0d..c091748 100644 --- a/audioflux/pwt.py +++ b/audioflux/pwt.py @@ -82,8 +82,6 @@ class PWT(Base): >>> # PWT can only input fft_length data >>> # For radix2_exp=12, then fft_length=4096 >>> audio_arr = audio_arr[:4096] - array([-5.5879354e-09, -9.3132257e-09, 0.0000000e+00, ..., - -1.3137090e-01, -1.5649168e-01, -1.8550715e-01], dtype=float32) Create PWT object of Octave @@ -101,19 +99,6 @@ class PWT(Base): >>> import numpy as np >>> spec_arr = obj.pwt(audio_arr) >>> spec_arr = np.abs(spec_arr) - array([[2.0927351e-04, 2.0927349e-04, 2.0927351e-04, ..., 2.0927351e-04, - 2.0927349e-04, 2.0927351e-04], - [3.3626966e-03, 3.3626966e-03, 3.3626969e-03, ..., 3.3626969e-03, - 3.3626969e-03, 3.3626966e-03], - [1.1016995e-03, 1.1016995e-03, 1.1016997e-03, ..., 1.1016995e-03, - 1.1016997e-03, 1.1016995e-03], - ..., - [4.2387177e-04, 4.2435329e-04, 4.2531796e-04, ..., 1.5332833e-03, - 1.5327019e-03, 1.5324111e-03], - [1.4914650e-05, 1.4527454e-05, 1.3769239e-05, ..., 3.9103269e-04, - 3.9109713e-04, 3.9112900e-04], - [2.5504729e-04, 2.5552284e-04, 2.5647049e-04, ..., 3.5153513e-04, - 3.5086548e-04, 3.5052933e-04]], dtype=float32) Show spectrogram plot @@ -136,6 +121,12 @@ def __init__(self, num, radix2_exp=12, samplate=32000, is_padding=True): super(PWT, self).__init__(pointer(OpaquePWT())) + self.fft_length = fft_length = 1 << radix2_exp + + # check num + if num > (fft_length // 2 + 1): + raise ValueError(f'num={num} is too large') + # check BPO if scale_type == SpectralFilterBankScaleType.OCTAVE and bin_per_octave < 1: raise ValueError(f'bin_per_octave={bin_per_octave} must be a positive integer') @@ -171,8 +162,6 @@ def __init__(self, num, radix2_exp=12, samplate=32000, self.normal_type = normal_type self.is_padding = is_padding - self.fft_length = 1 << radix2_exp - fn = self._lib['pwtObj_new'] fn.argtypes = [POINTER(POINTER(OpaquePWT)), c_int, c_int, POINTER(c_int), POINTER(c_float), POINTER(c_float), POINTER(c_int), diff --git a/audioflux/reassign.py b/audioflux/reassign.py index decbc9c..2cff48c 100644 --- a/audioflux/reassign.py +++ b/audioflux/reassign.py @@ -53,8 +53,6 @@ class Reassign(Base): >>> import audioflux as af >>> audio_path = af.utils.sample_path('220') >>> audio_arr, sr = af.read(audio_path) - array([-5.5879354e-09, -9.3132257e-09, 0.0000000e+00, ..., - 3.2826858e-03, 3.2447521e-03, 3.0795704e-03], dtype=float32) Create Reassign object diff --git a/audioflux/spectrogram.py b/audioflux/spectrogram.py index d024cce..b8a83a5 100644 --- a/audioflux/spectrogram.py +++ b/audioflux/spectrogram.py @@ -1501,13 +1501,9 @@ def novelty(self, m_data_arr, step=1, threshold=0., method_type: SpectralNoveltyMethodType Novelty method type. - See: `type.SpectralNoveltyMethodType` - data_type: SpectralNoveltyDataType Novelty data type. - See: `type.SpectralNoveltyDataType` - Returns ------- novelty: np.ndarray [shape=(time,)] @@ -1809,6 +1805,8 @@ class Spectrogram(SpectrogramBase): - The default value of `Deep/DeepChroma` is **hamm**, and others are **hann** - Deep/DeepChroma can only be set to `hamm/hann/rect` + See: `type.WindowType` + slide_length: int Window sliding length @@ -1817,17 +1815,23 @@ class Spectrogram(SpectrogramBase): It cat be set to mag or power. If you needs `db` type, you can set `power` type and then call the `power_to_db` method. + See: `type.SpectralDataType` + filter_bank_type: SpectralFilterBankType Spectral filter bank type. It determines the type of spectrogram, like Linear/Mel/Bank/Erb etc. filter_style_type: SpectralFilterBankStyleType Spectral filter bank style type. It determines the bank type of window. + see: `type.SpectralFilterBankStyleType` + filter_normal_type: SpectralFilterBankNormalType Spectral filter normal type. It determines the type of normalization. Linear/Chroma/Deep/DeepChroma is not provided. + See: `type.SpectralFilterBankNormalType` + Notes ----- * Chroma @@ -2357,7 +2361,6 @@ class Deep(SpectrogramBase): >>> from audioflux.spectrogram import Deep >>> spec_obj = Deep(num=84) >>> spec_arr = spec_obj.spectrogram(audio_arr) - >>> print(spec_arr.shape) Show plot diff --git a/audioflux/st.py b/audioflux/st.py index a9f44d2..06efb18 100644 --- a/audioflux/st.py +++ b/audioflux/st.py @@ -59,8 +59,6 @@ class ST(Base): >>> # ST can only input fft_length data >>> # For radix2_exp=12, then fft_length=4096 >>> audio_arr = audio_arr[:4096] - array([-5.5879354e-09, -9.3132257e-09, 0.0000000e+00, ..., - -1.3013066e-01, -8.8289484e-02, -7.2360471e-02], dtype=float32) Create ST object @@ -72,20 +70,6 @@ class ST(Base): >>> import numpy as np >>> spec_arr = obj.st(audio_arr) >>> spec_arr = np.abs(spec_arr) - >>> spec_arr - array([[0.00209793, 0.00209793, 0.00209793, ..., 0.00209793, 0.00209793, - 0.00209793], - [0.00145852, 0.00145853, 0.00145855, ..., 0.00145848, 0.00145849, - 0.00145851], - [0.00158793, 0.00158835, 0.00158878, ..., 0.00158667, 0.00158709, - 0.00158751], - ..., - [0.00688319, 0.00595584, 0.00489485, ..., 0.00790979, 0.00791093, - 0.00756201], - [0.00688484, 0.0059536 , 0.00489008, ..., 0.00792713, 0.00792307, - 0.00756865], - [0.00688638, 0.00595126, 0.00488525, ..., 0.00794437, 0.0079351 , - 0.00757518]], dtype=float32) Show spectrogram plot diff --git a/audioflux/swt.py b/audioflux/swt.py index 4ccb229..e1f7da6 100644 --- a/audioflux/swt.py +++ b/audioflux/swt.py @@ -70,20 +70,12 @@ class SWT(Base): >>> audio_arr, sr = af.read(audio_path) >>> audio_len = 4096 * 5 >>> audio_arr = audio_arr[:audio_len] - array([-5.5879354e-09, -9.3132257e-09, 0.0000000e+00, ..., - -1.3137090e-01, -1.5649168e-01, -1.8550715e-01], dtype=float32) Create SWT object >>> from audioflux.type import WaveletDiscreteType - >>> num = 5 - >>> fft_length = audio_len - >>> wavelet_type = WaveletDiscreteType.DB - >>> t1 = 4 - >>> t2 = 0 - >>> obj = af.SWT(num=num, fft_length=fft_length, - >>> wavelet_type=wavelet_type, - >>> t1=t1, t2=t2) + >>> obj = af.SWT(num=5, fft_length=audio_len, + >>> wavelet_type=WaveletDiscreteType.DB, t1=4, t2=0) Get SWT data @@ -97,6 +89,26 @@ class SWT(Base): >>> # af.write('SAVE_PATH_1.wav', save_data_arr1) >>> # af.write('SAVE_PATH_2.wav', save_data_arr2) + Show wave plot + + >>> import matplotlib.pyplot as plt + >>> from audioflux.display import fill_wave + >>> + >>> n_num, n_time = data_arr2.shape + >>> fig, ax = plt.subplots(nrows=n_num + 1, figsize=(8, 16), sharex=True, layout='tight') + >>> _ax0 = fill_wave(audio_arr[:n_time], samplate=sr, axes=ax[0]) + >>> _ax0.set_title('SWT arr2 origin') + >>> for i in range(n_num): + >>> _ax = fill_wave(data_arr2[i], samplate=sr, axes=ax[i + 1]) + >>> _ax.set_title(f'SWT arr2 num={i}') + >>> + >>> n_num, n_time = data_arr1.shape + >>> fig, ax = plt.subplots(nrows=n_num + 1, figsize=(8, 16), sharex=True, layout='tight') + >>> _ax0 = fill_wave(audio_arr[:n_time], samplate=sr, axes=ax[0]) + >>> _ax0.set_title('SWT arr1 origin') + >>> for i in range(n_num): + >>> _ax = fill_wave(data_arr1[i], samplate=sr, axes=ax[i + 1]) + >>> _ax.set_title(f'SWT arr1 num={i}') """ def __init__(self, num, fft_length, wavelet_type=WaveletDiscreteType.SYM, t1=4, t2=0): diff --git a/audioflux/synsq.py b/audioflux/synsq.py index dccaeb5..c29930c 100644 --- a/audioflux/synsq.py +++ b/audioflux/synsq.py @@ -46,8 +46,6 @@ class Synsq(Base): >>> # WSST can only input fft_length data >>> # For radix2_exp=12, then fft_length=4096 >>> audio_arr = audio_arr[:4096] - array([-5.5879354e-09, -9.3132257e-09, 0.0000000e+00, ..., - -1.3137090e-01, -1.5649168e-01, -1.8550715e-01], dtype=float32) Create CWT object of octave @@ -136,6 +134,8 @@ def synsq(self, m_data_arr, filter_bank_type, fre_arr): filter_bank_type: SpectralFilterBankScaleType bank type,need to correspond to `m_data_arr`. + See: `type.SpectralFilterBankScaleType` + fre_arr: np.ndarray [shape=(fre,)] fre band arr,need to correspond to `m_data_arr`. Use `get_fre_band_arr` method to get diff --git a/audioflux/temporal.py b/audioflux/temporal.py index 20be89a..453128b 100644 --- a/audioflux/temporal.py +++ b/audioflux/temporal.py @@ -28,6 +28,21 @@ class Temporal(Base): Window type for each frame. See: `type.WindowType` + + Examples + -------- + + Read 220Hz audio data + + >>> import audioflux as af + >>> audio_path = af.utils.sample_path('220') + >>> audio_arr, sr = af.read(audio_path) + + Create Temporal and extract feature + + >>> temp_obj = af.Temporal(frame_length=2048, slide_length=512) + >>> temp_obj.temporal(audio_arr) + >>> energy_arr, rms_arr, zero_cross_arr, m_arr = temp_obj.get_data(len(audio_arr)) """ def __init__(self, frame_length=2048, slide_length=512, window_type=WindowType.HANN): diff --git a/audioflux/type/basic.py b/audioflux/type/basic.py index 2c9351b..c181cf2 100644 --- a/audioflux/type/basic.py +++ b/audioflux/type/basic.py @@ -122,9 +122,9 @@ class SpectralFilterBankScaleType(Enum): """ Spectral Filter Bank Scale Type """ - #: Short-time Fourier transform. + #: Short-time Fourier transform spectrogram. LINEAR = 0 - #: Numbers evenly distributed over the specified interval. + #: Linspace-scale spectrogram. LINSPACE = 1 #: Mel-scale spectrogram. @@ -136,7 +136,7 @@ class SpectralFilterBankScaleType(Enum): #: Octave-scale spectrogram. OCTAVE = 5 # similar Constant-Q - #: Evenly distributed numbers on a logarithmic scale. + #: Logarithmic-scale spectrogram. LOG = 6 @@ -144,9 +144,9 @@ class SpectralFilterBankType(Enum): """ Spectral Filter Bank Type """ - #: Short-time Fourier transform. + #: Short-time Fourier transform spectrogram. LINEAR = 0 - #: Numbers evenly distributed over the specified interval. + #: Linspace-scale spectrogram. LINSPACE = 1 #: Mel-scale spectrogram. @@ -156,15 +156,15 @@ class SpectralFilterBankType(Enum): #: Erb-scale spectrogram. ERB = 4 - #: Octave-scale spectrogram. Similar Constant-Q. - OCTAVE = 5 - #: Evenly distributed numbers on a logarithmic scale. + #: Octave-scale spectrogram. + OCTAVE = 5 # similar Constant-Q + #: Logarithmic-scale spectrogram. LOG = 6 - #: Deep-scale spectrogram. Similar Constant-Q. + #: Deep-scale spectrogram. DEEP = 7 - #: STFT(Linear) chroma. + #: Linear(STFT) chroma. CHROMA = 8 #: Octave-scale chroma. Similar cqt-chroma. diff --git a/audioflux/wpt.py b/audioflux/wpt.py index d391601..2d727ec 100644 --- a/audioflux/wpt.py +++ b/audioflux/wpt.py @@ -71,8 +71,6 @@ class WPT(Base): >>> audio_path = af.utils.sample_path('880') >>> audio_arr, sr = af.read(audio_path) >>> audio_arr = audio_arr[:4096] - array([-5.5879354e-09, -9.3132257e-09, 0.0000000e+00, ..., - -1.3013066e-01, -8.8289484e-02, -7.2360471e-02], dtype=float32) Create WPT object @@ -86,35 +84,17 @@ class WPT(Base): >>> import numpy as np >>> coef_arr, m_data_arr = obj.wpt(audio_arr) >>> m_data_arr = np.abs(m_data_arr) - >>> coef_arr - array([ 3.6137632e-01, -2.3430534e-01, -2.9199639e-01, ..., - 4.3719047e-06, 5.2029663e-04, -7.2287242e-03], dtype=float32) - - >>> m_data_arr - array([[0.36137632, 0.36137632, 0.36137632, ..., 0.1472302 , 0.1472302 , - 0.1472302 ], - [0.07282908, 0.07282908, 0.07282908, ..., 0.07150738, 0.07150738, - 0.07150738], - [0.03349842, 0.03349842, 0.03349842, ..., 0.01227883, 0.01227883, - 0.01227883], - ..., - [0.00414347, 0.00414347, 0.00414347, ..., 0.00337636, 0.00337636, - 0.00337636], - [0.00756872, 0.00756872, 0.00756872, ..., 0.00425741, 0.00425741, - 0.00425741], - [0.00048361, 0.00048361, 0.00048361, ..., 0.00722872, 0.00722872, - 0.00722872]], dtype=float32) Show plot >>> import matplotlib.pyplot as plt >>> from audioflux.display import fill_spec, fill_plot - >>> fig, ax = plt.subplots(nrows=2, sharex=True) + >>> fig, ax = plt.subplots(nrows=2) >>> fill_spec(m_data_arr, axes=ax[0], >>> x_coords=obj.x_coords(), y_coords=obj.y_coords(), >>> x_axis='time', y_axis='log', - >>> title='WPT-abs') - >>> fill_plot(obj.x_coords()[1:], coef_arr, + >>> title='WPT') + >>> fill_plot(np.arange(len(coef_arr)), coef_arr, >>> axes=ax[1], label='WPT-coef') """ diff --git a/audioflux/wsst.py b/audioflux/wsst.py index c364e0f..f641f00 100644 --- a/audioflux/wsst.py +++ b/audioflux/wsst.py @@ -91,8 +91,6 @@ class WSST(Base): >>> # WSST can only input fft_length data >>> # For radix2_exp=12, then fft_length=4096 >>> audio_arr = audio_arr[:4096] - array([-5.5879354e-09, -9.3132257e-09, 0.0000000e+00, ..., - -1.3137090e-01, -1.5649168e-01, -1.8550715e-01], dtype=float32) Create WSST object of mel @@ -119,7 +117,7 @@ class WSST(Base): >>> x_coords=obj.x_coords(), >>> y_coords=obj.y_coords(), >>> x_axis='time', y_axis='log', - >>> title='CWT') + >>> title='CWT-Mel') >>> fig.colorbar(img, ax=ax) >>> >>> # Show WSST @@ -128,7 +126,7 @@ class WSST(Base): >>> x_coords=obj.x_coords(), >>> y_coords=obj.y_coords(), >>> x_axis='time', y_axis='log', - >>> title='WSST') + >>> title='WSST-Mel') >>> fig.colorbar(img, ax=ax) """ diff --git a/docs/benchmark.rst b/docs/benchmark.rst index b5acf9e..d67711a 100644 --- a/docs/benchmark.rst +++ b/docs/benchmark.rst @@ -4,6 +4,15 @@ Benchmark Server performance ------------------ +server hardware:: + + - CPU: AMD Ryzen Threadripper 3970X 32-Core Processor + - Memory: 128GB + +Using 128ms audio data(sampling rate: 32000). + +The total time spent on extracting features for 1000 times. + +--------+-----------+---------+-----------------+--------------------------+ | Lib | audioFlux | librosa | pyAudioAnalysis | python\_speech\_features | +========+===========+=========+=================+==========================+ @@ -15,13 +24,15 @@ Server performance +--------+-----------+---------+-----------------+--------------------------+ | Chroma | 0.155s | 2.174s | 1.287s | -- | +--------+-----------+---------+-----------------+--------------------------+ -| NSGT | 0.292 | -- | -- | -- | -+--------+-----------+---------+-----------------+--------------------------+ Mobile performance ------------------ +Using 128ms audio data(sampling rate: 32000). + +The time spent on extracting features each time. + +--------+---------------+----------+-----------+------------------+ | Method | iPhone 13 Pro | iPhone X | Honor V40 | OPPO Reno4 SE 5G | +========+===============+==========+===========+==================+ @@ -33,5 +44,3 @@ Mobile performance +--------+---------------+----------+-----------+------------------+ | Chroma | 0.354ms | 0.615ms | 0.803ms | 1.775ms | +--------+---------------+----------+-----------+------------------+ -| NSGT | 0.044ms | 0.084ms | 0.188ms | 0.615ms | -+--------+---------------+----------+-----------+------------------+ diff --git a/docs/comparison.rst b/docs/comparison.rst index 96fdfdf..e5cf84c 100644 --- a/docs/comparison.rst +++ b/docs/comparison.rst @@ -1,37 +1,44 @@ Comparison ========== -**1. BFT Scale** +BFT Scale +--------- .. image:: ./image/comparisonBFTScale.png :alt: comparisonBFTScale.png -**2. BFT Style** +BFT Style +--------- .. image:: ./image/comparisonBFTStyle.png :alt: comparisonBFTStyle.png -**3. CQT vs NSGT Scale** +CQT vs NSGT Scale +----------------- .. image:: ./image/comparisonCQT_NSGT.png :alt: comparisonCQT_NSGT.png -**4. CWT wavelet vs PWT** +CWT wavelet vs PWT +------------------ .. image:: ./image/comparisonCWT_PWT.png :alt: comparisonCWT_PWT.png -**5. Chroma** +Chroma +------ .. image:: ./image/comparisonChroma.png :alt: comparisonChroma.png -**6. XXCC** +XXCC +---- .. image:: ./image/comparisonXXCC.png :alt: comparisonXXCC.png -**7. 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