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A Geometrically Constrained ICA Algorithm for Blind Separation in Convolutive Environments
Michele Scarpiniti, Francesco Di Palma, Raffaele Parisi, Aurelio Uncini
79 - 88
10.3233/978-1-60750-692-8-79
Frontiers in Artificial Intelligence and Applications
Volume 226: Neural Nets WIRN10
Abstract

In this paper a blind source separation algorithm in convolutive environment is presented. In order to avoid the classical permutation ambiguity in the frequency domain solution, a geometrical constraint is considered. Moreover a beam-former algorithm is integrated with the proposed solution: in this way the directivity pattern of the proposed architecture can take into account the residual permutation at low frequencies and the scaling inconsistency. Several experimental results are shown to demonstrate the effectiveness of the proposed method.

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