An algorithm based on nonlinear PCA and regulation for blind source separation of convolutive mixtures

Authors:
Liyan Ma;Hongwei Li
Affiliations:
School of Mathematics and Physics, China University of Geosciences, Wuhan, China;School of Mathematics and Physics, China University of Geosciences, Wuhan, China
Venue:
LSMS'07 Proceedings of the Life system modeling and simulation 2007 international conference on Bio-Inspired computational intelligence and applications
Year:
2007

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Abstract

This paper proposes a method of blind separation which extracts independent signals from their convolutive mixtures. The function is acquired by modifying a network's parameters so that a cost function takes the minimum at anytime. Firstly we propose a regulation of a nonlinear principle component analysis (PCA) cost function for blind source separation of convolutive mixtures. Then by minimizing the cost function a new recursive least-squares (RLS) algorithm is developed in time domain, and we proposed two update equations for recursively computing the regularized factor. This algorithm has two stages: one is pre-whitening, the other is RLS iteration. Simulations show that our algorithm can successfully separate convolutive mixtures and has fast convergence rate.