Open Access

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

EURASIP Journal on Audio, Speech, and Music Processing20072007:092528

Received: 30 June 2006

Accepted: 24 January 2007

Published: 5 April 2007


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.


Authors’ Affiliations

School of Information Technology and Engineering, University of Ottawa


  1. Aboulnasr T, Mayyas K: Complexity reduction of the NLMS algorithm via selective coefficient update. IEEE Transactions on Signal Processing 1999,47(5):1421-1424. 10.1109/78.757235View ArticleGoogle Scholar
  2. Haykin S (Ed): Unsupervised Adaptive Filtering, Volume 1: Blind Source Separation. John Wiley & Sons, New York, NY, USA; 2000.Google Scholar
  3. Cichocki A, Amari S: Adaptive Blind Signal and Image Processing. John Wiley & Sons, New York, NY, USA; 2000.Google Scholar
  4. Hyvarinen A, Karhunen J, Oja E: Independent Component Analysis. John Wiley & Sons, New York, NY, USA; 2001.View ArticleGoogle Scholar
  5. Amari S, Douglas SC, Cichocki A, Yang HH: Multichannel blind deconvolution and equalization using the natural gradient. Proceedings of the 1st IEEE Signal Processing Workshop on Signal Processing Advances in Wireless Communications (SPAWC '97), April 1997, Paris, France 101-104.View ArticleGoogle Scholar
  6. Douglas SC, Sun X: Convolutive blind separation of speech mixtures using the natural gradient. Speech Communication 2003,39(1-2):65-78. 10.1016/S0167-6393(02)00059-6View ArticleMATHGoogle Scholar
  7. Smaragdis P: Blind separation of convolved mixtures in the frequency domain. Neurocomputing 1998,22(1–3):21-34.View ArticleMATHGoogle Scholar
  8. Parra L, Spence C: Convolutive blind separation of non-stationary sources. IEEE Transactions on Speech and Audio Processing 2000,8(3):320-327. 10.1109/89.841214View ArticleMATHGoogle Scholar
  9. Sawada H, Mukai R, Araki S, Makino S: A robust and precise method for solving the permutation problem of frequency-domain blind source separation. IEEE Transactions on Speech and Audio Processing 2004,12(5):530-538. 10.1109/TSA.2004.832994View ArticleGoogle Scholar
  10. Ikram MZ, Morgan DR: A beamforming approach to permutation alignment for multichannel frequency-domain blind speech separation. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '02), May 2002, Orlando, Fla, USA 1: 881-884.Google Scholar
  11. Khong AWH, Naylor PA: Stereophonic acoustic echo cancellation employing selective-tap adaptive algorithms. IEEE Transactions on Audio, Speech and Language Processing 2006,14(3):785-796.View ArticleGoogle Scholar
  12. Werner S, de Campos MLR, Diniz PSR: Partial-update NLMS algorithms with data-selective updating. IEEE Transactions on Signal Processing 2004,52(4):938-949. 10.1109/TSP.2004.823483MathSciNetView ArticleGoogle Scholar
  13. Pitas I: Fast algorithms for running ordering and max/min calculation. IEEE Transactions on Circuits and Systems 1989,36(6):795-804. 10.1109/31.90400View ArticleGoogle Scholar
  14. Makino S, Sawada H, Mukai R, Araki S: Blind source separation of convolutive mixtures of speech in frequency domain. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences 2005,E88-A(7):1640-1654. 10.1093/ietfec/e88-a.7.1640View ArticleGoogle Scholar
  15. ITU-T Recommend P.862 : Perceptual evaluation of speech quality (PESQ), an objective method for end-to end speech quality assessment of narrowband telephone network and speech codecs. 2000.Google Scholar


© Q. Pan and T. Aboulnasr. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.