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Blind separation of linear convolutive mixtures through parallel stochastic optimization

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2 Author(s)
Cohen, M. ; Dept. of Electr. & Comput. Eng., Johns Hopkins Univ., Baltimore, MD, USA ; Cauwenberghs, G.

We apply stochastic parallel optimization techniques to on-line blind separation of linear convolutive mixtures of independent time-varying signals. The optimization performs stochastic gradient descent on a scalar measure of statistical independence observed directly on the outputs of the unmixing network, which contains a matrix of finite impulse response (FIR) filters. We derive on-line adaptation rules, and a scalable modular architecture with minimum memory requirements amenable to parallel VLSI implementation. The architecture implements a slight modification of the network adaptation rule, which omits symmetrical non-causal terms in the computation of the stochastic gradient. Simulations indicate near-perfect separation using both versions of the rule, with a minimum phase response resulting from the simplified version

Published in:

Circuits and Systems, 1998. ISCAS '98. Proceedings of the 1998 IEEE International Symposium on  (Volume:3 )

Date of Conference:

31 May-3 Jun 1998