Matchmaker is a Python library for real-time music alignment.
Music alignment is a fundamental MIR task, and real-time music alignment is a necessary component of many interactive applications (e.g., automatic accompaniment systems, automatic page turning).
Unlike offline alignment methods, for which state-of-the-art implementations are publicly available, real-time (online) methods have no standard implementation, forcing researchers and developers to build them from scratch for their projects.
We aim to provide efficient reference implementations of score followers for use in real-time applications which can be easily integrated into existing projects.
Setting up the code as described here requires conda. Follow the instructions for your OS.
To setup the experiments, use the following script.
# Clone matchmaker
git clone https://github.com/CarlosCancino-Chacon/matchmaker.git
cd matchmaker
# Create the conda environment
conda env create -f environment.yml
# Install matchmaker
pip install -e .If you have a ImportError with 'Fluidsynth' by pyfluidsynth library on MacOS, please refer to the following link.
To get started quickly, you can use the Matchmaker class, which provides a simple interface for running the alignment process. You can use a musicxml or midi file as the score file. Specify "audio" or "midi" as the input_type argument, and the default device for that input type will be automatically set up. For options regarding the method, please refer to the Alignment Methods section.
from matchmaker import Matchmaker
mm = Matchmaker(
score_file="path/to/score",
input_type="audio",
method="dixon",
)
for current_position in mm.run():
print(current_position) # beat position in the scoreThe returned value is the current position in the score, represented in beats defined by partitura library's note array system.
Specifically, each position is calculated for every frame input and interpolated within the score's onset_beat array.
Please refer to here for more information about the onset_beat concept.
You can simulate the real-time alignment by putting a specific performance file as input, rather than running it as a live stream.
The type of performance file can be either audio file or midi file, depending on the input_type.
from matchmaker import Matchmaker
mm = Matchmaker(
score_file="path/to/score",
performance_file="path/to/performance.mid",
input_type="midi",
feature_type="mel",
method="hmm",
)
for current_position in mm.run():
print(current_position) # beat position in the scoreTo use a specific audio or MIDI device that is not the default device, you can pass the device name or index.
from matchmaker import Matchmaker
mm = Matchmaker(
score_file="path/to/score",
input_type="audio",
feature_type="chroma",
method="arzt",
device_name_or_index="MacBookPro Microphone",
)
for current_position in mm.run():
print(current_position) # beat position in the scoreIf you want to use a different alignment method or custom method, you can do so by importing the specific class and passing the necessary parameters.
In order to define a custom alignment class, you need to inherit from the Base OnlineAlignment class and implement the run method. Note that the returned value from the OnlineAlignment class should be the current frame number in the reference features, not in beats.
from matchmaker.dp import OnlineTimeWarpingDixon
from matchmaker.io.audio import AudioStream
from matchmaker.features import ChromagramProcessor
feature_processor = ChromagramProcessor()
reference_features = feature_processor('path/to/score/audio.wav')
with AudioStream(processor=feature_processor) as stream:
score_follower = OnlineTimeWarpingDixon(reference_features, stream.queue)
for current_frame in score_follower.run():
print(current_frame) # frame number in the reference featuresMatchmaker currently supports the following alignment methods:
"dixon": On-line time warping algorithm by S. Dixon (2005)."arzt": On-line time warping algorithm adapted from Brazier and Widmer (2020) (based on the work by Arzt et al. (2010))"hmm": Hidden Markov Model-based score follower by Cancino-Chacón et al. (2023), based on the state-space score followers by Duan et al. (2011) and Jiang and Raphael (2020).
Matchmaker currently supports the following feature types:
- For audio:
"chroma": Chroma features. Default feature type for audio input."mfcc": Mel-frequency cepstral coefficients."mel": Mel-Spectrogram."logspectral": Log-spectral features used in Dixon (2005).
- For MIDI:
pianoroll: Piano-roll features. Default feature type for MIDI input."pitch": Pitch features for MIDI input."pitchclass": Pitch class features for MIDI input.
Initialization parameters for the Matchmaker class:
score_file(str): Path to the score file.input_type(str): Type of input data. Options:"audio","midi".feature_type(str): Type of feature to use. Options:"chroma","mfcc","cqt","spectrogram","onset".method(str): Alignment method to use. Options:"dixon","arzt","hmm".sample_rate(int): Sample rate of the input audio data.frame_rate(int): Frame rate of the input audio/MIDI data.device_name_or_index(str or int): The audio/MIDI device name or index you want to use. IfNone, the default device will be used.
If you find Matchmaker useful, we would appreciate if you could cite us!
@inproceedings{matchmaker_lbd,
title={{Matchmaker: A Python library for Real-time Music Alignment}},
author={Park, Jiyun and Cancino-Chac\'{o}n, Carlos and Kwon, Taegyun and Nam, Juhan},
booktitle={{Proceedings of the Late Breaking/Demo Session at the 25th International Society for Music Information Retrieval Conference}},
address={San Francisco, USA.},
year={2024}
}
This work has been supported by the Austrian Science Fund (FWF), grant agreement PAT 8820923 ("Rach3: A Computational Approach to Study Piano Rehearsals"). Additionally, this work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2023R1A2C3007605).
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.