Underdetermined blind source separation based on relaxed sparsity condition of sources

Authors:
Dezhong Peng;Yong Xiang
Affiliations:
School of Engineering and Information Technology, Deakin University, Geelong, VIC, Australia;School of Engineering and Information Technology, Deakin University, Geelong, VIC, Australia
Venue:
IEEE Transactions on Signal Processing
Year:
2009

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Abstract

Recently, Aissa-El-Bey et al, have proposed two subspace-based 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 sub matrix 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.