A flexible ICA-based method for AEC without requiring double-talk detection

Marko Kanadi; Muhammad Tahir Akhtar; Wataru Mitsuhashi

A flexible ICA-based method for AEC without requiring double-talk detection

Marko Kanadi, Muhammad Tahir Akhtar, Wataru Mitsuhashi

Research output: Contribution to conference › Paper › peer-review

Abstract

In this paper, we propose the use of the flexible independent component analysis (ICA) algorithm for the acoustic echo cancellation (AEC). The flexible ICA algorithm has a parametric score function which is controlled by the Gaussianity of the source signal (speech in our case). The probability density function of the speech signal is not always the super Gaussian, hence the inflexible score function used in the existing ICAbased AEC system is not always suitable. On the other hand, the proposed method is able to always provide the suitable score function depending on the present Gaussianity of the speech signal. The experimental results indicate that the proposed method significantly outperforms the existing ICA-based AEC algorithm and the variable step-size (VSS)-NLMS algorithm.

Original language	English
Pages	67-73
Number of pages	7
Publication status	Published - Dec 1 2011
Externally published	Yes
Event	Asia-Pacific Signal and Information Processing Association Annual Summit and Conference 2011, APSIPA ASC 2011 - Xi'an, China Duration: Oct 18 2011 → Oct 21 2011

Conference

Conference	Asia-Pacific Signal and Information Processing Association Annual Summit and Conference 2011, APSIPA ASC 2011
Country	China
City	Xi'an
Period	10/18/11 → 10/21/11

ASJC Scopus subject areas

Information Systems
Signal Processing

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Cite this

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title = "A flexible ICA-based method for AEC without requiring double-talk detection",

abstract = "In this paper, we propose the use of the flexible independent component analysis (ICA) algorithm for the acoustic echo cancellation (AEC). The flexible ICA algorithm has a parametric score function which is controlled by the Gaussianity of the source signal (speech in our case). The probability density function of the speech signal is not always the super Gaussian, hence the inflexible score function used in the existing ICAbased AEC system is not always suitable. On the other hand, the proposed method is able to always provide the suitable score function depending on the present Gaussianity of the speech signal. The experimental results indicate that the proposed method significantly outperforms the existing ICA-based AEC algorithm and the variable step-size (VSS)-NLMS algorithm.",

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AU - Kanadi, Marko

AU - Akhtar, Muhammad Tahir

AU - Mitsuhashi, Wataru

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N2 - In this paper, we propose the use of the flexible independent component analysis (ICA) algorithm for the acoustic echo cancellation (AEC). The flexible ICA algorithm has a parametric score function which is controlled by the Gaussianity of the source signal (speech in our case). The probability density function of the speech signal is not always the super Gaussian, hence the inflexible score function used in the existing ICAbased AEC system is not always suitable. On the other hand, the proposed method is able to always provide the suitable score function depending on the present Gaussianity of the speech signal. The experimental results indicate that the proposed method significantly outperforms the existing ICA-based AEC algorithm and the variable step-size (VSS)-NLMS algorithm.

AB - In this paper, we propose the use of the flexible independent component analysis (ICA) algorithm for the acoustic echo cancellation (AEC). The flexible ICA algorithm has a parametric score function which is controlled by the Gaussianity of the source signal (speech in our case). The probability density function of the speech signal is not always the super Gaussian, hence the inflexible score function used in the existing ICAbased AEC system is not always suitable. On the other hand, the proposed method is able to always provide the suitable score function depending on the present Gaussianity of the speech signal. The experimental results indicate that the proposed method significantly outperforms the existing ICA-based AEC algorithm and the variable step-size (VSS)-NLMS algorithm.

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