Double Sparsity: Towards Blind Estimation of Multiple Channels

Prasad Sudhakar; Simon Arberet; Rémi Gribonval

inria-00537756, version 1

Double Sparsity: Towards Blind Estimation of Multiple Channels

Prasad Sudhakar ()^a^, 1, Simon Arberet ()^b^, 2, Rémi Gribonval ()^a^, 1

Latent Variable Analysis and Signal Separation, 9th International Conference on (LVA/ICA2010) 6365 (2010) 571--578

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.

a – INRIA
b – EPFL
1: METISS (INRIA - IRISA)
CNRS : UMR6074 – INRIA – Institut National des Sciences Appliquées (INSA) - Rennes – Université de Rennes 1
2: Ecole Polytechnique Fédérale de Lausanne (EPFL)
École Polytechnique Fédérale de Lausanne

inria-00537756, version 1
http://hal.inria.fr/inria-00537756
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From: Rémi Gribonval <>
Submitted on: Monday, 22 November 2010 09:34:03
Updated on: Wednesday, 1 December 2010 16:13:23

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