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Underdetermined blind source separation based on relaxed sparsity condition of sources

Peng, Dezhong and Xiang, Yong 2009, Underdetermined blind source separation based on relaxed sparsity condition of sources, IEEE transactions on signal processing, vol. 57, no. 2, pp. 809-814.

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Title Underdetermined blind source separation based on relaxed sparsity condition of sources
Author(s) Peng, Dezhong
Xiang, Yong
Journal name IEEE transactions on signal processing
Volume number 57
Issue number 2
Start page 809
End page 814
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2009-02
ISSN 1053-587X
1941-0476
Keyword(s) eigenvalue
eigenvector
time-frequency distribution
underdetermined blind source separation
Summary Recently, Aissa-El-Bey et al. have proposed two subspacebased methods for underdetermined blind source separation (UBSS) in time-frequency (TF) domain. These methods allow multiple active sources at TF points so long as the number of active sources at any TF point is strictly less than the number of sensors, and the column vectors of the mixing matrix are pairwise linearly independent. In this correspondence, we first show that the subspace-based methods must also satisfy the condition that any M × M submatrix of the mixing matrix is of full rank. Then we present a new UBSS approach which only requires that the number of active sources at any TF point does not exceed that of sensors. An algorithm is proposed to perform the UBSS.
Language eng
Field of Research 090609 Signal Processing
Socio Economic Objective 890104 Mobile Telephone Networks and Services
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
HERDC collection year 2009
Copyright notice ©2008, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30028097

Document type: Journal Article
Collection: School of Engineering
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Created: Thu, 01 Apr 2010, 16:25:43 EST by Sandra Dunoon