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Underdetermined blind source separation based on relaxed sparsity condition of sources
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.
History
Journal
IEEE transactions on signal processingVolume
57Issue
2Pagination
809 - 814Publisher
IEEELocation
Piscataway, N.J.Publisher DOI
ISSN
1053-587XeISSN
1941-0476Language
engPublication classification
C1 Refereed article in a scholarly journal; C Journal articleCopyright notice
2008, IEEEUsage metrics
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