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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Lee, I., Kim, T., & Lee, T. W. (2006). Complex fastIVA: A robust maximum likelihood approach of MICA for convolutive BSS. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3889 LNCS, pp. 625–632). Springer Verlag. https://doi.org/10.1007/11679363_78
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