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Sparse multichannel source separation using incoherent K-SVD method

Abolghasemi, V, Ferdowsi, S and Sanei, S (2011) Sparse multichannel source separation using incoherent K-SVD method In: SSP '11, 2011-06-28 - 2011-06-30, Nice, France.

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Abstract

In this paper the problem of sparse source separation of linear mixtures is addressed. We propose to apply K-SVD, which is a leading dictionary learning method, for this purpose. Further, a modified gradient-based K-SVD scheme for incoherent dictionary learning and source separation is proposed. The promising results on random synthetic signals reveal the ability of this technique for utilizing in source separation framework. We also suggest BOLD detection fMRI as an application for this method. The preliminary results confirm the successful separation of this type of data.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
AuthorsEmailORCID
Abolghasemi, VUNSPECIFIEDUNSPECIFIED
Ferdowsi, SUNSPECIFIEDUNSPECIFIED
Sanei, SUNSPECIFIEDUNSPECIFIED
Date : 2011
Identification Number : 10.1109/SSP.2011.5967736
Contributors :
ContributionNameEmailORCID
PublisherIEEE, UNSPECIFIEDUNSPECIFIED
Depositing User : Symplectic Elements
Date Deposited : 14 Jan 2013 10:37
Last Modified : 23 Sep 2013 19:56
URI: http://epubs.surrey.ac.uk/id/eprint/742928

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