Doubly sparse models for multiple filter estimation in sparse echoic environments

Prasad Sudhakar 1 Simon Arberet 2 Rémi Gribonval 1 Pierre Vandergheynst 2
1 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 estimation of multiple time-domain sparse filters from echoic mixtures of several unknown sources, when the sources are sparse in the time-frequency domain. We propose a sparse filter estimation framework consisting of two steps: a) a clustering step to group the time-frequency points of mixtures where only one source is active, for each source; b) a convex optimisation step to estimate the filters based on a time-frequency domain cross-relation. We propose a new wideband formulation of a frequency domain cross-relation, besides the one based on classical narrowband approximation. The solutions of the convex optimisation problem, formed using the cross-relation, are characterised. Numerical evaluation shows the benefit of using the wideband cross-relation for sparse echoic filter estimation. Further, the potential of the proposed framework for blind estimation of sparse echoic filters is demonstrated in a controlled experimental setting where in the proposed approach outperforms the state of the art blind filter estimation techniques, when the filters are sufficiently sparse.
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Soumis le : lundi 10 décembre 2012 - 12:45:50
Dernière modification le : jeudi 11 janvier 2018 - 06:20:09
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Prasad Sudhakar, Simon Arberet, Rémi Gribonval, Pierre Vandergheynst. Doubly sparse models for multiple filter estimation in sparse echoic environments. [Research Report] 2012. 〈hal-00763226〉

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