Extract-Framewise-Spectral-Spread-Skewness-Kurtosis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import essentia.standard as es\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import librosa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load audio files\n",
    "pluck1 = es.MonoLoader(filename='/Users/adamdeedman/Documents/University/Lectures/Third Year/FYP/Python Stage/IDMT-SMT-BASS/PS/FS/BS_1_EQ_1_FS_NO_1_10.wav')()\n",
    "pick1 = es.MonoLoader(filename='/Users/adamdeedman/Documents/University/Lectures/Third Year/FYP/Python Stage/IDMT-SMT-BASS/PS/PK/BS_1_EQ_1_PK_NO_1_10.wav')()\n",
    "slap1 = es.MonoLoader(filename='/Users/adamdeedman/Documents/University/Lectures/Third Year/FYP/Python Stage/IDMT-SMT-BASS/PS/ST/BS_1_EQ_1_ST_NO_1_10.wav')()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make sure audio signals have even number of samples\n",
    "if len(pluck1) % 2 == 1:\n",
    "    pick1 = np.append(pluck1, 0)\n",
    "\n",
    "\n",
    "if len(pick1) % 2 == 1:\n",
    "    pick1 = np.append(pick1, 0)\n",
    "\n",
    "\n",
    "if len(slap1) % 2 == 1:\n",
    "    pick1 = np.append(slap1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set frame size and hop size\n",
    "\n",
    "frame_size = 2048\n",
    "hop_size = 1024"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate magnitude spectrum frames\n",
    "\n",
    "pluck1_spec = es.Spectrum(size=frame_size)  # Initialize spectrum algorithm\n",
    "pluck1_frame_generator = es.FrameGenerator(pluck1, frameSize=frame_size, hopSize=hop_size, startFromZero=True)  # Initialize frame generator\n",
    "pluck1_spec_frames = [pluck1_spec(frame) for frame in pluck1_frame_generator]\n",
    "\n",
    "pick1_spec = es.Spectrum(size=frame_size)  # Initialize spectrum algorithm\n",
    "pick1_frame_generator = es.FrameGenerator(pick1, frameSize=frame_size, hopSize=hop_size, startFromZero=True)  # Initialize frame generator\n",
    "pick1_spec_frames = [pick1_spec(frame) for frame in pick1_frame_generator]\n",
    "\n",
    "slap1_spec = es.Spectrum(size=frame_size)  # Initialize spectrum algorithm\n",
    "slap1_frame_generator = es.FrameGenerator(slap1, frameSize=frame_size, hopSize=hop_size, startFromZero=True)  # Initialize frame generator\n",
    "slap1_spec_frames = [slap1_spec(frame) for frame in slap1_frame_generator]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate central moments, spread, skewness, and kurtosis for each frame\n",
    "\n",
    "pluck1_features = []\n",
    "for frame in pluck1_spec_frames:\n",
    "    pluck1_spread, pluck1_skewness, pluck1_kurtosis = es.DistributionShape()(es.CentralMoments()(frame))\n",
    "    pluck1_features.append([pluck1_spread, pluck1_skewness, pluck1_kurtosis])\n",
    "\n",
    "pick1_features = []\n",
    "for frame in pick1_spec_frames:\n",
    "    pick1_spread, pick1_skewness, pick1_kurtosis = es.DistributionShape()(es.CentralMoments()(frame))\n",
    "    pluck1_features.append([pick1_spread, pick1_skewness, pick1_kurtosis])\n",
    "\n",
    "slap1_features = []\n",
    "for frame in slap1_spec_frames:\n",
    "    slap1_spread, slap1_skewness, slap1_kurtosis = es.DistributionShape()(es.CentralMoments()(frame))\n",
    "    slap1_features.append([slap1_spread, slap1_skewness, slap1_kurtosis])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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[0.04009893536567688, 2.7366504669189453, 6.904566764831543], [0.049816571176052094, 2.2774710655212402, 4.3268656730651855], [0.04760150983929634, 2.5990922451019287, 5.713128089904785], [0.06066771596670151, 1.8342360258102417, 2.320021152496338], [0.03313802182674408, 3.209057092666626, 9.893998146057129]]\n"
     ]
    }
   ],
   "source": [
    "print(pluck1_features)"
   ]
  }
 ],
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