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2002•Unpublished venueRequires access

A NLMS-like adaptive filtering algorithm of decorrelating stereophonic signal

Yuanjian Zhou, Shengli Xie

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Abstract

A NLMS-like (Normalized Least Mean Square) adaptive filtering algorithm with decorrelation ability is proposed. We not only prove the convergence of the new algorithm, but also analyze its decorrelation ability. Simulation shows that it has a better effect than Benesty (1996) and Sankran (1999) when applied to stereophonic acoustic echo cancellation.

About this research paper

What this paper is about

A NLMS-like (Normalized Least Mean Square) adaptive filtering algorithm with decorrelation ability is proposed. We not only prove the convergence of the new algorithm, but also analyze its decorrelation ability. Simulation shows that it has a better effect than Benesty (1996) and Sankran (1999) when applied to stereophonic acoustic echo cancellation.

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Method / approach

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Available abstract

A NLMS-like (Normalized Least Mean Square) adaptive filtering algorithm with decorrelation ability is proposed. We not only prove the convergence of the new algorithm, but also analyze its decorrelation ability. Simulation shows that it has a better effect than Benesty (1996) and Sankran (1999) when applied to stereophonic acoustic echo cancellation.

Key concepts: Decorrelation, Stereophonic sound, Adaptive filter, Convergence (economics), Algorithm, Computer science, Least mean squares filter, SIGNAL (programming language)

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