lapd-python/singingvoiceseparationrpca

Singing-Voice Separation From Monaural Recordings Using Robust Principal Component Analysis

Singing-Voice Separation From Monaural Recordings Using Robust Principal Component

This package contains the Matlab codes implementing the RPCA source separation algorithm described in "Singing-Voice Separation From Monaural Recordings Using Robust Principal Component Analysis," ICASSP 2012.

Our algorithm is composed of the following parts:

• STFT, masking
• Robust Principal Component solved by using inexact augmented Lagrange multiplier (ALM) Method Reference: http://perception.csl.uiuc.edu/matrix-rank/sample_code.html
• ISTFT
• BSS Eval toolbox Version 2.1 Reference: http://bass-db.gforge.inria.fr/bss_eval/

The algorithm achieves the state-of-the-art performance on MIR-1K Dataset in an unsupervised way.

Demo

Run rpca_mask_demo.m to see how the functions are called. Change RUN_EVALUATION = 0 if you don't need evaluation.

For more information, please check: https://sites.google.com/site/singingvoiceseparationrpca/

License

MIT