Double sparsity: towards blind estimation of multiple channels

Authors:
Prasad Sudhakar;Simon Arberet;Rémi Gribonval
Affiliations:
Centre de recherche INRIA Rennes - Bretagne Atlantique, Rennes CEDEX, France;Institute of Electrical Engineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland;Centre de recherche INRIA Rennes - Bretagne Atlantique, Rennes CEDEX, France
Venue:
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Year:
2010

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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.