Complex FastIVA: a robust maximum likelihood approach of MICA for convolutive BSS

Authors:
Intae Lee;Taesu Kim;Te-Won Lee
Affiliations:
Institute for Neural Computation, University of California, San Diego, CA;Institute for Neural Computation, University of California, San Diego, CA;Institute for Neural Computation, University of California, San Diego, CA
Venue:
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Year:
2006

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Abstract

We tackle the frequency-domain blind source separation problem in a way to avoid permutation correction. By exploiting the facts that the frequency components of a signal have some dependency and that the mixing of sources is restricted to each frequency bin, we apply the concept of multidimensional independent component analysis to the problem and propose a new algorithm that separates independent groups of dependent source components. We introduce general entropic contrast functions for this analysis and a corresponding likelihood function with a multidimensional prior that models the dependent frequency components. We assume circularity for the complex variables and derive a fast algorithm by applying Newton’s method learning rule. The algorithm separates mixed sources even in very challenging acoustic settings.