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Removal of residual crosstalk components in blind source separation using LMS filters

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4 Author(s)
Mukai, Ryo ; NTT Commun. Sci. Labs., NTT Corp., Kyoto, Japan ; Araki, S. ; Sawada, H. ; Makino, S.

The performance of blind source separation (BSS) using independent component analysis (ICA) declines significantly in a reverberant environment. The degradation is mainly caused by the residual crosstalk components derived from the reverberation of the jammer signal. This paper describes a post-processing method designed to refine output signals obtained by BSS. We propose a new method which uses LMS filters in the frequency domain to estimate the residual crosstalk components in separated signals. The estimated components are removed by non-stational spectral subtraction. The proposed method removes the residual components precisely, thus it compensates for the weakness of BSS in a reverberant environment. Experimental results using speech signals show that the proposed method improves the signal-to-interference ratio by 3 to 5 dB.

Published in:

Neural Networks for Signal Processing, 2002. Proceedings of the 2002 12th IEEE Workshop on

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