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Time-Domain Convolutive Blind Source Separation Employing Selective-Tap Adaptive Algorithms

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Abstract

We investigate novel algorithms to improve the convergence and reduce the complexity of time-domain convolutive blind source separation (BSS) algorithms. First, we propose MMax partial update time-domain convolutive BSS (MMax BSS) algorithm. We demonstrate that the partial update scheme applied in the MMax LMS algorithm for single channel can be extended to multichannel time-domain convolutive BSS with little deterioration in performance and possible computational complexity saving. Next, we propose an exclusive maximum selective-tap time-domain convolutive BSS algorithm (XM BSS) that reduces the interchannel coherence of the tap-input vectors and improves the conditioning of the autocorrelation matrix resulting in improved convergence rate and reduced misalignment. Moreover, the computational complexity is reduced since only half of the tap inputs are selected for updating. Simulation results have shown a significant improvement in convergence rate compared to existing techniques.

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Correspondence to Qiongfeng Pan.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Pan, Q., Aboulnasr, T. Time-Domain Convolutive Blind Source Separation Employing Selective-Tap Adaptive Algorithms. J AUDIO SPEECH MUSIC PROC. 2007, 092528 (2007) doi:10.1155/2007/92528

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Keywords

  • Coherence
  • Autocorrelation
  • Computational Complexity
  • Convergence Rate
  • Acoustics