ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Multichannel nonnegative matrix factorization in convolutive mixtures for audio source separation
IEEE Transactions on Audio, Speech, and Language Processing
A watermarking-based method for informed source separation of audio signals with a single sensor
IEEE Transactions on Audio, Speech, and Language Processing
Performance measurement in blind audio source separation
IEEE Transactions on Audio, Speech, and Language Processing
Audio source separation with a single sensor
IEEE Transactions on Audio, Speech, and Language Processing
Informed source separation through spectrogram coding and data embedding
Signal Processing
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We address the issue of source separation in a particular informed configuration where both the sources and the mixtures are assumed to be known during a so-called encoding stage. This knowledge enables the computation of a side information which ought to be small enough to be watermarked in the mixtures. At the decoding stage, the sources are no longer assumed to be known, only the mixtures and the side information are processed to perform source separation. The proposed method models the sources jointly using latent variables in a framework close to multichannel nonnegative matrix factorization and models the mixing process as linear filtering. Separation at the decoding stage is done using generalized Wiener filtering of the mixtures. An experimental setup shows that the method gives very satisfying results with mixtures composed of many sources. A study of its performance with respect to the number of latent variables is presented.