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Volume 19, Issue 1
Frequency Domain Acoustic Echo Cancellation Using Auxiliary Function Based Independent Component Analysis

Lifu Wu, Lei Wang, Xinnian Sun & Shuaiheng Sun

DOI: 10.4208/JICS-2024-004

J. Info. Comput. Sci. , 19 (2024), pp. 53-64.

[An open-access article; the PDF is free to any online user.]

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  • Abstract

The performance of traditional Acoustic Echo Cancellation (AEC) is restricted due to the double-talk detector it used to determine the double-talk and single-talk scenarios. While Blind Source Separation (BSS) signal model is a full duplex model with both far-end and near-end signals, thus the BSS-based AEC does not need the double-talk detector. This paper adopts Auxiliary function based Independent Component Analysis (Aux-ICA) algorithm to realize acoustic echo cancellation in frequency domain, in which the object function is minimizing the mutual information, and the auxiliary function technique is used for optimization. Simulation results show that this method has lower computational complexity and better performance in acoustic echo cancellation under continuous double-talk scenarios.

  • Keywords

Echo cancellation, Auxiliary function, Independent Component Analysis (ICA), Blind source separation, Double-talk.

  • AMS Subject Headings

62H25, 68W40

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{JICS-19-53, author = {Wu , LifuWang , LeiSun , Xinnian and Sun , Shuaiheng}, title = {Frequency Domain Acoustic Echo Cancellation Using Auxiliary Function Based Independent Component Analysis}, journal = {Journal of Information and Computing Science}, year = {2024}, volume = {19}, number = {1}, pages = {53--64}, abstract = {

The performance of traditional Acoustic Echo Cancellation (AEC) is restricted due to the double-talk detector it used to determine the double-talk and single-talk scenarios. While Blind Source Separation (BSS) signal model is a full duplex model with both far-end and near-end signals, thus the BSS-based AEC does not need the double-talk detector. This paper adopts Auxiliary function based Independent Component Analysis (Aux-ICA) algorithm to realize acoustic echo cancellation in frequency domain, in which the object function is minimizing the mutual information, and the auxiliary function technique is used for optimization. Simulation results show that this method has lower computational complexity and better performance in acoustic echo cancellation under continuous double-talk scenarios.

}, issn = {3080-180X}, doi = {https://doi.org/10.4208/JICS-2024-004}, url = {http://global-sci.org/intro/article_detail/jics/23879.html} }
TY - JOUR T1 - Frequency Domain Acoustic Echo Cancellation Using Auxiliary Function Based Independent Component Analysis AU - Wu , Lifu AU - Wang , Lei AU - Sun , Xinnian AU - Sun , Shuaiheng JO - Journal of Information and Computing Science VL - 1 SP - 53 EP - 64 PY - 2024 DA - 2024/03 SN - 19 DO - http://doi.org/10.4208/JICS-2024-004 UR - https://global-sci.org/intro/article_detail/jics/23879.html KW - Echo cancellation, Auxiliary function, Independent Component Analysis (ICA), Blind source separation, Double-talk. AB -

The performance of traditional Acoustic Echo Cancellation (AEC) is restricted due to the double-talk detector it used to determine the double-talk and single-talk scenarios. While Blind Source Separation (BSS) signal model is a full duplex model with both far-end and near-end signals, thus the BSS-based AEC does not need the double-talk detector. This paper adopts Auxiliary function based Independent Component Analysis (Aux-ICA) algorithm to realize acoustic echo cancellation in frequency domain, in which the object function is minimizing the mutual information, and the auxiliary function technique is used for optimization. Simulation results show that this method has lower computational complexity and better performance in acoustic echo cancellation under continuous double-talk scenarios.

Wu , LifuWang , LeiSun , Xinnian and Sun , Shuaiheng. (2024). Frequency Domain Acoustic Echo Cancellation Using Auxiliary Function Based Independent Component Analysis. Journal of Information and Computing Science. 19 (1). 53-64. doi:10.4208/JICS-2024-004
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