This toolbox provides MATLAB and Python implementations of the Multi-resolution Common Fate Transform. The MCFT of an audio signal outputs a four-dimensional representation that make the spectro-temporal modulation patterns of the signal explicit in the audio representation.
By making the modulation patterns explicity represented as additional dimensions, the MCFT increases the separability of audio mixtures composed of sources with significant time-frequency overlap and different modulation patterns (e.g. two voices singing in unison, each having differernt vibrato). This allows effective source separation for audio scenes where approaches that work on time-frequency representations (e.g. magnitude spectrograms) fail, as shown in this journal article.
The MCFT toolbox uses the CQT implementation by Schörkhuber et al. to compute the time-frequency representation of the input audio signal.
Audio examples, time-frequency plots, and detailed experimental results are provided in the demo webpage.
If you are using the MCFT for your research, please cite it using one of the following bibtex citations:
@article{pishdadian2018multi,
title={Multi-resolution common fate transform},
author={Pishdadian, Fatemeh and Pardo, Bryan},
journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
volume={27},
number={2},
pages={342--354},
year={2018},
publisher={IEEE}
}
@inproceedings{pishdadian2017multi,
title={A multi-resolution approach to common fate-based audio separation},
author={Pishdadian, Fatemeh and Pardo, Bryan and Liutkus, Antoine},
booktitle={2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={566--570},
year={2017},
organization={IEEE}
}
The MCFT toolbox is under an MIT License
MIT License
Copyright (c) 2019 Interactive Audio Lab
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