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Blind source separation in a noisy environment using super-exponential algorithm

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6 Author(s)
M. Ito ; Graduate Sch. of Inf. Sci., Nagoya Univ., Japan ; M. Kawamoto ; M. Ohata ; T. Mukai
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"Super-exponential" methods (SEMs) are attractive algorithms for solving blind signal separation problems. Conventional SEMs are so sensitive to Gaussian noise that they cannot work in a noisy environment. To overcome this drawback, we proposed a new SEM , which does not utilise second-order statistics but only higher-order cumulants. Hence, the proposed SEM becomes robust to Gaussian noise (RSEM). In this paper, mixed signals in a noisy environment are separated in the frequency domain using an adaptive version of RSEM (ARSEM). After separation, noise components are reduced with a speech enhancement technique. We show the results of this simulation and experiment, which demonstrates the effectiveness of the proposed method

Published in:

Proceedings of the Fifth IEEE International Symposium on Signal Processing and Information Technology, 2005.

Date of Conference:

21-21 Dec. 2005