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A compressed sensing approach for underdetermined blind audio source separation with sparse representation

Xu, T and Wang, W (2009) A compressed sensing approach for underdetermined blind audio source separation with sparse representation In: SSP '09, 2009-08-31 - 2009-09-03, Cardiff, UK.

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Abstract

The problem of underdetermined blind audio source separation is usually addressed under the framework of sparse signal representation. In this paper, we develop a novel algorithm for this problem based on compressed sensing which is an emerging technique for efficient data reconstruction. The proposed algorithm consists of two stages. The unknown mixing matrix is firstly estimated from the audio mixtures in the transform domain, as in many existing methods, by a K-means clustering algorithm. Different from conventional approaches, in the second stage, the sources are recovered by using a compressed sensing approach. This is motivated by the similarity between the mathematical models adopted in compressed sensing and source separation. Numerical experiments including the comparison with a recent sparse representation approach are provided to show the good performance of the proposed method.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Authors :
AuthorsEmailORCID
Xu, TUNSPECIFIEDUNSPECIFIED
Wang, WUNSPECIFIEDUNSPECIFIED
Date : 6 October 2009
Identification Number : https://doi.org/10.1109/SSP.2009.5278532
Contributors :
ContributionNameEmailORCID
PublisherIEEE, UNSPECIFIEDUNSPECIFIED
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 14:43
Last Modified : 28 Mar 2017 14:43
URI: http://epubs.surrey.ac.uk/id/eprint/596098

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