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A Wideband Doubly-Sparse Approach for MITO Sparse Filter Estimation

Simon Arberet 1 Prasad Sudhakar 2 Rémi Gribonval 2
2 METISS - Speech and sound data modeling and processing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : We propose an approach for the estimation of sparse filters from a convolutive mixture of sources, exploiting the time-domain sparsity of the mixing filters and the sparsity of the sources in the time-frequency (TF) domain. The proposed approach is based on a wideband formulation of the cross-relation (CR) in the TF domain and on a framework including two steps: (a) a clustering step, to determine the TF points where the CR is valid; (b) a filter estimation step, to recover the set of filters associated with each source. We propose for the first time a method to blindly perform the clustering step (a) and we show that the proposed approach based on the wideband CR outperforms the narrowband approach and the GCC-PHAT approach by between 5 dB and 20 dB.
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https://hal.inria.fr/inria-00567210
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Simon Arberet, Prasad Sudhakar, Rémi Gribonval. A Wideband Doubly-Sparse Approach for MITO Sparse Filter Estimation. Acoustics, Speech and Signal Processing, IEEE International Conference on (ICASSP 2011), May 2011, Prague, Czech Republic. To appear, ⟨10.1109/ICASSP.2011.5947085⟩. ⟨inria-00567210v2⟩

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