The tools to solve Audio Chord Recognition (Chord Estimation) problem.
Some of pre-trained model metrics of major and minor chords made by mir_eval on Isophonics and Robbie Williams datasets.
| _ | majmin | mirex |
|---|---|---|
| beatles | 0.792 | 0.771 |
| queen | 0.815 | 0.798 |
| zweieck | 0.839 | 0.811 |
| robbie_williams | 0.908 | 0.885 |
chord-recognition takes an audio file in mp3 format, and then represents it as waweform in numpy.array. It computes STFT to split the input into frames representing 1.5 seconds of the audio. It applies a simple ConvNet to each frame to classify into 25 classes (12 minor, 12 major plus a non-chord class). Finally the result is enhanced by HMM smoothing.
pip install git+https://github.com/discort/chord-recognition
from chord_recognition import estimate_chords
result = estimate_chords(audio_path='tests/fixtures/C_Am.mp3', nonchord=True)
print(result)
[(0.0, 1.6718, 'N'),
(1.6718, 3.7152, 'C'),
(3.7152, 4.2725, 'N'),
(4.2725, 6.0372, 'Am')]Use this link to download Beatles, Queen and Robbie Williams datasets in isophonics format
unzip data.zip data
pip install -r requirements.txt
py.test -q --cov=chord_recognition tests
python -m chord_recognition.evaluate -ds <dataset_name>
audio_analysis.ipynb <- Check spectrogram, chromagram, etc
chords_analysis.ipynb <- Check chord distribution and other stats data
experiments.ipynb <- Describing some experiments to improve classification/
experiment<N>.ipynb <- A code to reproduce an experiment
jupyter notebook
References:
- Müller M. (2015) Chord Recognition. In: Fundamentals of Music Processing
- Korzeniowski, Widmer (2016) A Fully Convolutional Deep Auditory Model for Musical Chord Recognition
- Zanoni (2014) Chord and Harmony annotations of the first five albums by Robbie Williams