Special Issue on Computational Intelligence in Signal and Image Processing; Guest Editors: Yen-Wei Chen and Vasile PaladeNo Access

REAL-TIME BLIND SOURCE SEPARATION OF ACOUSTIC SIGNALS WITH A RECURSIVE APPROACH

SHUXUE DING

School of Computer Science and Engineering, The University of Aizu, Tsuruga, Ikki-machi, Aizu-Wakamatsu City, Fukushima 965-8580, Japan

Brain Science Institute, RIKEN, Saitama 351-0198, Japan

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JIE HUANG

School of Computer Science and Engineering, The University of Aizu, Tsuruga, Ikki-machi, Aizu-Wakamatsu City, Fukushima 965-8580, Japan

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DAMING WEI

School of Computer Science and Engineering, The University of Aizu, Tsuruga, Ikki-machi, Aizu-Wakamatsu City, Fukushima 965-8580, Japan

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https://doi.org/10.1142/S1469026804001252Cited by:1 (Source: Crossref)

Abstract

We propose an approach for real-time blind source separation (BSS), in which the observations are linear convolutive mixtures of statistically independent acoustic sources. A recursive least square (RLS)-like strategy is devised for real-time BSS processing. A normal equation is further introduced as an expression between the separation matrix and the correlation matrix of observations. We recursively estimate the correlation matrix and explicitly, rather than stochastically, solve the normal equation to obtain the separation matrix. As an example of application, the approach has been applied to a BSS problem where the separation criterion is based on the second-order statistics and the non-stationarity of signals in the frequency domain. In this way, we realise a novel BSS algorithm, called exponentially weighted recursive BSS algorithm. The simulation and experimental results showed an improved separation and a superior convergence rate of the proposed algorithm over that of the gradient algorithm. Moreover, this algorithm can converge to a much lower cost value than that of the gradient algorithm.

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