Double Sparsity: Towards Blind Estimation of Multiple Channels

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.


Published in:
Proceedings of the 9th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA 2010)
Presented at:
9th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA 2010), St. Malo, France, September 27-30, 2010
Year:
2010
Publisher:
Springer-Verlag's
Keywords:
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 Record created 2010-08-18, last modified 2018-03-17

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