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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Lee, I., Kim, T., Lee, TW. (2006). Complex FastIVA: A Robust Maximum Likelihood Approach of MICA for Convolutive BSS. In: Rosca, J., Erdogmus, D., PrÃncipe, J.C., Haykin, S. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2006. Lecture Notes in Computer Science, vol 3889. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11679363_78
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DOI: https://doi.org/10.1007/11679363_78
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32630-4
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