Estimating multiple filters from stereo mixtures: a double sparsity approach

We consider the problem of estimating multiple filters from convolutive mixtures of several unknown sources. We propose to exploit both the time-frequency (TF) sparsity of the sources and the sparsity of the mixing filters. Our framework consists of: a) a clustering step to group the TF points where only one source is active, for each source; b) a convex optimisation step, to estimate the filters using TF cross-relations that capture linear constraints satisfied by the unknown filters. Experiments demonstrate that the approach is well suited for the estimation of sufficiently sparse filters.


Published in:
Workshop : Signal Processing with Adaptive Sparse Structured Representations (SPARS)
Presented at:
Workshop : Signal Processing with Adaptive Sparse Structured Representations (SPARS), Edinburgh, Scotland., June 27-30, 2011
Year:
2011
Keywords:
Laboratories:




 Record created 2011-04-26, last modified 2018-09-13


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