A novel approach for underdetermined blind sources separation in frequency domain

Authors:
Ming Xiao;Shengli Xie;Yuli Fu
Affiliations:
School of Electrics & Information Engineering, South China University of Technology, Guangzhou and Department of Electrics & Information Engineering, Maoming College, Guangdong, China;School of Electrics & Information Engineering, South China University of Technology, Guangzhou, Guangdong, China;School of Electrics & Information Engineering, South China University of Technology, Guangzhou, Guangdong, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Year:
2005

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Abstract

In this paper, we discussed the separation of n sources from m linear mixtures when the underlying system is underdetermined, that is, when m n. The underdetermined blind sources separation has two steps. In matrix-recovery step, we defined a characteristic of the signals as the durative-sparsity and proposed a novel approach called as a searching-and-averaging-based method in frequency domain. This approach tells us how to search some data points that are very close to the basis lines along the direction of basis vectors a j and how to use them to estimate the mixing matrix. In source-recovery step, we used Bofill and Zibulevsky's shortest-path algorithm. Finally, the separation results were obtained using their short-time Fourier transforms.