Non negative sparse representation for Wiener based source separation with a single sensor

Laurent Benaroya 1 Lorcan Mcdonagh 1 Frédéric Bimbot 1 Rémi Gribonval 1
1 METISS - Speech and sound data modeling and processing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : We propose a new method to perform the separation of two sound sources from a single sensor. This method generalizes the Wiener filtering with locally stationary, non gaussian, parametric source models. The method involves a learning phase for which we propose three different algorithm. In the separation phase, we use a sparse non negative decomposi- tion algorithm of our own. The algorithms are evaluated on the separation of real audio data.
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Laurent Benaroya, Lorcan Mcdonagh, Frédéric Bimbot, Rémi Gribonval. Non negative sparse representation for Wiener based source separation with a single sensor. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2003), Apr 2003, Hong-Kong, Hong Kong SAR China. pp.VI/613--VI/616, ⟨10.1109/ICASSP.2003.1201756⟩. ⟨inria-00574784⟩

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