Source Separation of Piano Concertos This repository provides a pipeline for decomposing piano concerto recordings into separate piano and orchestral tracks. Our approach investigates open-source spectrogram- and waveform-based approaches as well as hybrid models operating in both spectrogram and waveform domains.

Installation

We recommend to do this inside a conda or virtual environment (requiring at least Python 3.8). As an alternative, you may also create the environment pc-separation as provided by the file environment.yml (which includes the jupyter package to run the demo files):

conda env create -f environment.yml

Separating piano concertos

The notebook Separator.ipynb showcases an exemplary application. It includes downloading pretrained weights of UMX, SPL, DMC, and HDMC models and the test dataset PCD are also provided in the notebook.

For further information and to listen to audio examples, please visit our demo website.

Unison data generation

To be continued 👻

Training

To be continued 👻

References

[1] Y. Özer and M. Müller “Source Separation of Piano Concertos Using Musically Motivated Augmentation Techniques,“ in IEEE/ACM Transactions on Audio, Speech, and Language Processing (TASLP), 32: 1214–1225, 2024.

[2] Y. Özer and M. Müller, “Source separation of piano concertos with test-time adaptation,“ in Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Bengaluru, India, 2022, pp. 493–500.

[3] Y. Özer, S. Schwär, V. Arifi-Müller, J. Lawrence, E. Sen, and M. Müller, “Piano Concerto Dataset (PCD): A multitrack dataset of piano concertos,“ Transactions of the International Society for Music Information Retrieval (TISMIR), vol. 6, no. 1, pp. 75–88, 2023.

[4] F.-R. Stöter, S. Uhlich, A. Liutkus, and Y. Mitsufuji, “Open-Unmix – A reference implementation for music source separation,“ Journal of Open Source Software, vol. 4, no. 41, 2019.

[5] R. Hennequin, A. Khlif, F. Voituret, and M. Moussallam, “Spleeter: a fast and efficient music source separation tool with pre-trained models,“ Journal of Open Source Software, vol. 5, no. 50, p. 2154, 2020, Deezer Research.

[6] A. Défossez, N. Usunier, L. Bottou, and F. R. Bach, “Music source separation in the waveform domain,“ 2019.

[7] A. Défossez, “Hybrid spectrogram and waveform source separation,“ in Proceedings of the ISMIR 2021 Workshop on Music Source Separation, Online, 2021.

[8] Meinard Müller, Yigitcan Özer, Michael Krause, Thomas Prätzlich, and Jonathan Driedger. “Sync Toolbox: A Python Package for Efficient, Robust, and Accurate Music Synchronization,“ Journal of Open Source Software (JOSS), 6(64), 2021.

[9] T. Prätzlich, J. Driedger, and M. Müller, “Memory-restricted multiscale dynamic time warping,” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Shanghai, China, March 2016, pp. 569–573.

[10] S. Böck, F. Korzeniowski, J. Schlüter, F. Krebs, and G. Widmer, “madmom: A new Python audio and music signal processing library,“ in Proceedings of the ACM International Conference on Multimedia (ACM-MM), Amsterdam, The Netherlands, 2016, pp. 1174–1178.

[11] F.-R. Stöter, S. Bayer, and B. Edler, “Unison source separation,” in Proceedings of the International Conference on Digital Audio Effects (DAFx), Erlangen, Germany, 2014, pp. 235–241.