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ICASSP
2009
IEEE

Multichannel nonnegative matrix factorization in convolutive mixtures. With application to blind audio source separation

11 years 3 months ago
Multichannel nonnegative matrix factorization in convolutive mixtures. With application to blind audio source separation
We consider inference in a general data-driven object-based model of multichannel audio data, assumed generated as a possibly underdetermined convolutive mixture of source signals. Each source is given a model inspired from nonnegative matrix factorization (NMF) with the Itakura-Saito divergence, which underlies a statistical model of superimposed Gaussian components. We address estimation of the mixing and source parameters using two methods. The first one consists of maximizing the exact joint likelihood of the multichannel data using an expectation-maximization algorithm. The second method consists of maximizing the sum of individual likelihoods of all channels using a multiplicative update algorithm inspired from NMF methodology. Our decomposition algorithms were applied to stereo music and assessed in terms of blind source separation performance.
Alexey Ozerov, Cédric Févotte
Added 17 Aug 2010
Updated 17 Aug 2010
Type Conference
Year 2009
Where ICASSP
Authors Alexey Ozerov, Cédric Févotte
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