A Recurrent ICA Approach to a Novel BSS Convolutive Nonlinear Problem | Zendy

Daniele Vigliano | Zendy; Raffaele Parisi | Zendy; Aurelio Uncini | Zendy

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A Recurrent ICA Approach to a Novel BSS Convolutive Nonlinear Problem

Author(s) -

Daniele Vigliano,

Raffaele Parisi,

Aurelio Uncini

Publication year - 2006

Publication title -

lecture notes in computer science

Language(s) - English

Resource type - Book series

SCImago Journal Rank - 0.249

H-Index - 400

eISSN - 1611-3349

pISSN - 0302-9743

ISBN - 3-540-33183-2

DOI - 10.1007/11731177_9

Subject(s) - blind signal separation , computer science , nonlinear system , independent component analysis , artificial intelligence , mixing (physics) , algorithm , mutual information , pattern recognition (psychology) , artificial neural network , speech recognition , telecommunications , channel (broadcasting) , physics , quantum mechanics

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.

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