Double Sparsity: Towards Blind Estimation of Multiple Channels

Prasad Sudhakar 1 Simon Arberet 2 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 framework for blind multiple filter estimation from convolutive mixtures, exploiting the time-domain sparsity of the mixing filters and the disjointness of the sources in the time-frequency domain. The proposed framework includes two steps: (a) a clustering step, to determine the frequencies where each source is active alone; (b) a filter estimation step, to recover the filter associated to each source from the corresponding incomplete frequency information. We show how to solve the filter estimation step (b) using convex programming, and we explore numerically the factors that drive its performance. Step (a) remains challenging, and we discuss possible strategies that will be studied in future work.
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Submitted on : Monday, November 22, 2010 - 9:34:03 AM
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Prasad Sudhakar, Simon Arberet, Rémi Gribonval. Double Sparsity: Towards Blind Estimation of Multiple Channels. Latent Variable Analysis and Signal Separation, 9th International Conference on (LVA/ICA2010), INRIA, Sep 2010, St Malo, France. pp.571--578. ⟨inria-00537756⟩



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