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.
Liste complète des métadonnées

Cited literature [8 references]  Display  Hide  Download

https://hal.inria.fr/inria-00537756
Contributor : Rémi Gribonval <>
Submitted on : Monday, November 22, 2010 - 9:34:03 AM
Last modification on : Friday, November 16, 2018 - 1:22:28 AM
Document(s) archivé(s) le : Wednesday, February 23, 2011 - 2:32:40 AM

File

LVA_ICA10.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : inria-00537756, version 1

Citation

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⟩

Share

Metrics

Record views

454

Files downloads

206