Under-determined source separation via mixed-norm regularized minimization

Matthieu Kowalski 1 Emmanuel Vincent 2 Rémi Gribonval 2
2 METISS - Speech and sound data modeling and processing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : We consider the problem of extracting the source signals from an under-determined convolutive mixture assuming that the mixing filters are known. We wish to exploit the sparsity and approximate disjointness of the time-frequency representations of the sources. However, classical time-frequency masking techniques cannot be directly applied due to the convolutive nature of the mixture. To address this problem, we first formulate it as the minimization of a functional combining a classical 2 discrepancy term between the observed mixture and the mixture reconstructed from the estimated sources and a sparse regularization term defined in terms of mixed 2 / 1 norms of source coefficients in a time-frequency domain. The minimum of the functional is then obtained by a thresholded Landweber iteration algorithm. Preliminary results are discussed for two synthetic audio mixtures.
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Communication dans un congrès
European Signal Processing Conference 2008, Aug 2008, Lausanne, Switzerland. pp.1569103109, 2008
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Matthieu Kowalski, Emmanuel Vincent, Rémi Gribonval. Under-determined source separation via mixed-norm regularized minimization. European Signal Processing Conference 2008, Aug 2008, Lausanne, Switzerland. pp.1569103109, 2008. 〈hal-00347089〉

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