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  • Research Article
  • Open Access

Exploiting Narrowband Efficiency for Broadband Convolutive Blind Source Separation

EURASIP Journal on Advances in Signal Processing20062007:016381

  • Received: 28 September 2005
  • Accepted: 11 June 2006
  • Published:


Based on a recently presented generic broadband blind source separation (BSS) algorithm for convolutive mixtures, we propose in this paper a novel algorithm combining advantages of broadband algorithms with the computational efficiency of narrowband techniques. By selective application of the Szegö theorem which relates properties of Toeplitz and circulant matrices, a new normalization is derived as a special case of the generic broadband algorithm. This results in a computationally efficient and fast converging algorithm without introducing typical narrowband problems such as the internal permutation problem or circularity effects. Moreover, a novel regularization method for the generic broadband algorithm is proposed and subsequently also derived for the proposed algorithm. Experimental results in realistic acoustic environments show improved performance of the novel algorithm compared to previous approximations.


  • Information Technology
  • Quantum Information
  • Computational Efficiency
  • Previous Approximation
  • Regularization Method

Authors’ Affiliations

Multimedia Communications and Signal Processing, University of Erlangen-Nuremberg, Cauerstraβe 7, Erlangen, 91058, Germany


  1. Hyvaerinen A, Karhunen J, Oja E: Independent Component Analysis. John Wiley & Sons, New York, NY, USA; 2001.View ArticleGoogle Scholar
  2. Buchner H, Aichner R, Kellermann W: TRINICON: a versatile framework for multichannel blind signal processing. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '04), May 2004, Montreal, Quebec, Canada 3: 889–892.Google Scholar
  3. Buchner H, Aichner R, Kellermann W: A generalization of blind source separation algorithms for convolutive mixtures based on second-order statistics. IEEE Transactions on Speech and Audio Processing 2005,13(1):120-134.View ArticleGoogle Scholar
  4. Buchner H, Aichner R, Kellermann W: Blind source separation for convolutive mixtures: a unified treatment. In Audio Signal Processing for Next-Generation Multimedia Communication Systems. Edited by: Huang Y, Benesty J. Kluwer Academic, Boston, Mass, USA; 2004:255-293.View ArticleGoogle Scholar
  5. Aichner R, Buchner H, Yan F, Kellermann W: A real-time blind source separation scheme and its application to reverberant and noisy acoustic environments. Signal Processing 2006,86(6):1260-1277. 10.1016/j.sigpro.2005.06.022View ArticleGoogle Scholar
  6. Haykin S: Adaptive Filter Theory. 4th edition. Prentice Hall, Englewood Cliffs, NJ, USA; 2002.MATHGoogle Scholar
  7. Markel JD, Gray AH: Linear Prediction of Speech. Springer, Berlin, Germany; 1976.View ArticleGoogle Scholar
  8. Aichner R, Buchner H, Kellermann W: On the causality problem in time-domain blind source separation and deconvolution algorithms. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '05), March 2005, Philadelphia, Pa, USA 5: 181–184.Google Scholar
  9. 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 ArticleGoogle Scholar
  10. Gray RM: On the asymptotic eigenvalue distribution of Toeplitz matrices. IEEE Transactions on Information Theory 1972,18(6):725-730. 10.1109/TIT.1972.1054924MathSciNetView ArticleGoogle Scholar
  11. Sherman PJ: Circulant approximations of the inverses of Toeplitz matrices and related quantities with applications to stationary random processes. IEEE Transactions on Acoustics, Speech, and Signal Processing 1985,33(6):1630-1632. 10.1109/TASSP.1985.1164723View ArticleGoogle Scholar


© Robert Aichner et al. 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.