A recurrent ICA approach to a novel BSS convolutive nonlinear problem

Authors:
Daniele Vigliano;Raffaele Parisi;Aurelio Uncini
Affiliations:
Dipartimento INFOCOM, Università di Roma “La Sapienza”, Roma, Italy;Dipartimento INFOCOM, Università di Roma “La Sapienza”, Roma, Italy;Dipartimento INFOCOM, Università di Roma “La Sapienza”, Roma, Italy
Venue:
WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
Year:
2005

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Abstract

This paper introduces a Recurrent Flexible ICA approach to a novel blind sources separation problem in convolutive nonlinear environment. The proposed algorithm performs the separation after the convolutive mixing of post nonlinear convolutive mixtures. The recurrent neural network produces the separation by minimizing the output mutual information. Experimental results are described to show the effectiveness of the proposed technique.