Efficient Multichannel NLMS Implementation for Acoustic Echo Cancellation

  • Fredric Lindstrom
  • Christian Schüldt
  • Ingvar Claesson
Open Access
Research Article
Part of the following topical collections:
  1. Adaptive Partial-Update and Sparse System Identification

Abstract

An acoustic echo cancellation structure with a single loudspeaker and multiple microphones is, from a system identification perspective, generally modelled as a single-input multiple-output system. Such a system thus implies specific echo-path models (adaptive filter) for every loudspeaker to microphone path. Due to the often large dimensionality of the filters, which is required to model rooms with standard reverberation time, the adaptation process can be computationally demanding. This paper presents a selective updating normalized least mean square (NLMS)-based method which reduces complexity to nearly half in practical situations, while showing superior convergence speed performance as compared to conventional complexity reduction schemes. Moreover, the method concentrates the filter adaptation to the filter which is most misadjusted, which is a typically desired feature.

Keywords

Acoustics Adaptive Filter Complexity Reduction Identification Perspective Reverberation Time 

References

  1. 1.
    Hänsler E, Schmidt G: Acoustic Echo and Noise Control: A Practical Approach. John Wiley & Sons, New York, NY, USA; 2004.CrossRefGoogle Scholar
  2. 2.
    Sondhi MM: An adaptive echo canceler. Bell System Technical Journal 1967,46(3):497-510.CrossRefGoogle Scholar
  3. 3.
    Widrow B, Stearns SD: Adaptive Signal Processing. Prentice-Hall, Englewood Cliffs, NJ, USA; 1985.MATHGoogle Scholar
  4. 4.
    Haykin S: Adaptive Filter Theory. 4th edition. Prentice-Hall, Englewood Cliffs, NJ, USA; 2002.MATHGoogle Scholar
  5. 5.
    Douglas SC: Adaptive filters employing partial updates. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 1997,44(3):209-216. 10.1109/82.558455CrossRefGoogle Scholar
  6. 6.
    Aboulnasr T, Mayyas K: Complexity reduction of the NLMS algorithm via selective coefficient update. IEEE Transactions on Signal Processing 1999,47(5):1421-1424. 10.1109/78.757235CrossRefGoogle Scholar
  7. 7.
    Naylor PA, Sherliker W: A short-sort M-Max NLMS partial-update adaptive filter with applications to echo cancellation. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '03), April 2003, Hong Kong 5: 373-376.Google Scholar
  8. 8.
    Dogançay K, Tanrikulu O: Adaptive filtering algorithms with selective partial updates. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 2001,48(8):762-769. 10.1109/82.959866CrossRefMATHGoogle Scholar
  9. 9.
    Schertler T: Selective block update of NLMS type algorithms. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '98), May 1998, Seattle, Wash, USA 3: 1717-1720.Google Scholar
  10. 10.
    Godavarti M, Hero AO III: Partial update LMS algorithms. IEEE Transactions on Signal Processing 2005,53(7):2382-2399.MathSciNetCrossRefGoogle Scholar
  11. 11.
    Hänsler E, Schmidt G: Single-channel acoustic echo cancellation. In Adaptive Signal Processing. Edited by: Benesty J, Huang Y. Springer, New York, NY, USA; 2003.Google Scholar
  12. 12.
    Kuo SM, Chen J: Multiple-microphone acoustic echo cancellation system with the partial adaptive process. Digital Signal Processing 1993,3(1):54-63. 10.1006/dspr.1993.1007MathSciNetCrossRefGoogle Scholar
  13. 13.
    Gollamudi S, Kapoor S, Nagaraj S, Huang Y-F: Set-membership adaptive equalization and an updator-shared implementation for multiple channel communications systems. IEEE Transactions on Signal Processing 1998,46(9):2372-2385. 10.1109/78.709523CrossRefGoogle Scholar
  14. 14.
    Werner S, Apolinario JA Jr., de Campos MLR, Diniz PSR: Low-complexity constrained affine-projection algorithms. IEEE Transactions on Signal Processing 2005,53(12):4545-4555.MathSciNetCrossRefGoogle Scholar
  15. 15.
    Gardner WA: Learning characteristics of stochastic-gradient-descent algorithms: a general study, analysis, and critique. Signal Processing 1984,6(2):113-133. 10.1016/0165-1684(84)90013-6MathSciNetCrossRefGoogle Scholar
  16. 16.
    ADSP-BF533 Blackfin processor hardware reference, Analog Devices, Norwood, Mass, USA, 2005Google Scholar

Copyright information

© Fredric Lindstrom 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.

Authors and Affiliations

  • Fredric Lindstrom
    • 1
  • Christian Schüldt
    • 2
  • Ingvar Claesson
    • 2
  1. 1.Konftel ABResearch and DevelopmentUmeaSweden
  2. 2.Department of Signal ProcessingBlekinge Institute of TechnologyRonnebySweden

Personalised recommendations