Abstract
Blind Source Separation (BSS) is a basic problem in signal processing. In this paper, we present a new method for separating convolutive mixtures based on the minimization of the output mutual information. We also introduce the concept of joint score function, and derive its relationship with marginal score function and independence. The new approach for minimizing the mutual information is very efficient, although limited by multivariate distribution estimations.
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© 2001 Springer-Verlag Berlin Heidelberg
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Babaie-Zadeh, M., Jutten, C., Nayebi, K. (2001). Separating Convolutive Mixtures by Mutual Information Minimization. In: Mira, J., Prieto, A. (eds) Bio-Inspired Applications of Connectionism. IWANN 2001. Lecture Notes in Computer Science, vol 2085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45723-2_101
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DOI: https://doi.org/10.1007/3-540-45723-2_101
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