Skip to main content
SpringerLink
Log in

GSVD-Based Optimal Filtering for Multi-Microphone Speech Enhancement

  • Chapter
Microphone Arrays

Part of the book series: Digital Signal Processing ((DIGSIGNAL))

Abstract

In this chapter a class of multi-microphone signal enhancement techniques is described, which are based on a Generalized Singular Value Decomposition (GSVD) and which amount to a specific optimal filtering problem when the so-called desired response signal cannot be observed. When applying this GSVD-based optimal filtering technique to noise reduction in multi-microphone speech recordings, simulations show that this technique has a better noise reduction performance than standard beamforming techniques for all reverberation times and that it is more robust to deviations from the nominal situation, e.g. encountered in uncalibrated microphone arrays. If the GSVD-based procedure is used to create speech as well as noise references, as in a Generalized Sidelobe Canceler (GSC) structure, and an adaptive noise canceler (ANC) is added, the noise reduction performance can still be improved. Because computational complexity may appear as a bottleneck for this algorithm, recursive GSVD-updating and downsampling techniques are needed to make this signal enhancement technique amenable to real-time implementation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. M. Dendrinos, S. Bakamidis, and G. Carayannis, “Speech enhancement from noise: A regenerative approach,” Speech Communication, vol. 10, no. 2, pp. 45–57, Feb. 1991.

    Article  Google Scholar 

  2. Y. Ephraim and H. L. Van Trees, “A Signal Subspace Approach for Speech Enhancement,” IEEE Trans. Speech, Audio Processing, vol. 3, no. 4, pp. 251266, July 1995.

    Google Scholar 

  3. S. H. Jensen, P. C. Hansen, S. D. Hansen, and J. A. Sorensen, “Reduction of Broad-Band Noise in Speech by Truncated QSVD,” IEEE Trans. Speech, Audio Processing, vol. 3, no. 6, pp. 439–448, Nov. 1995.

    Article  Google Scholar 

  4. U. Mittal and N. Phamdo, “Signal/Noise KLT Based Approach for Enhancing Speech Degraded by Colored Noise,” IEEE Trans. Speech, Audio Processing, vol. 8, no. 2, pp. 159–167, Mar. 2000.

    Article  Google Scholar 

  5. F. Asano, S. Hayamizu, T. Yamada, and S. Nakamura, “Speech Enhancement Based on the Subspace Method,” IEEE Trans. Speech, Audio Processing, vol. 8, no. 5, pp. 497–507, Sept. 2000.

    Article  Google Scholar 

  6. S. Dodo and M. Moonen, “SVD-based optimal filtering with applications to noise reduction in speech signals,” in Proc. of the IEEE Workshop Applicat. Signal Processing to Audio and Acoust. (WAS PAA’99), New Paltz, NY, USA, Oct. 1999, pp. 143–146.

    Google Scholar 

  7. S. Dodo and M. Moonen, “Robustness of SVD-based Optimal Filtering for Noise Reduction in Multi-Microphone Speech Signals,” in Proc. of the 1999 IEEE Int. Workshop Acoust. Echo and Noise Control (IWAENC’99), Pocono Manor, PA, USA, Sept. 1999, pp. 80–83.

    Google Scholar 

  8. S. Dodo and M. Moonen, “Noise Reduction in Multi-Microphone Speech Signals using Recursive and Approximate GSVD-based Optimal Filtering,” in Proc. IEEE Benelux Signal Processing Symp. (SPS2000), Hilvarenbeek, The Netherlands, Mar. 2000.

    Google Scholar 

  9. S. Dodo, E. De Clippel, and M. Moonen, “Multi-microphone noise reduction using GSVD-based optimal filtering with ANC postprocessing stage,” in Proc. of DSP2000 Workshop, Hunt, TX, USA, Oct. 2000.

    Google Scholar 

  10. S. Dodo, E. De Clippel, and M. Moonen, “Combined Acoustic Echo and Noise Reduction using GSVD-based Optimal Filtering,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing (ICASSP2000), Istanbul, Turkey, Istanbul, 2000, vol. 2, pp. 1061–1064.

    Google Scholar 

  11. S. Van Gerven and F. Xie, “A Comparative Study of Speech Detection Methods,” in Proc. EUROSPEECH,Rhodos, Greece, Sept. 1997, vol. 3, pp. 10951098.

    Google Scholar 

  12. S. G. Tanyer and H. Ozer, “Voice activity detection in nonstationary noise,” IEEE Trans. Speech, Audio Processing, vol. 8, no. 4, pp. 478–482, July 2000.

    Article  Google Scholar 

  13. F. T. Luk, “A parallel method for computing the generalized singular value decomposition,” Internat. J. Parallel Distr. Comp., vol. 2, pp. 250–260, 1985.

    Article  Google Scholar 

  14. G. H. Golub and C. F. Van Loan, Matrix Computations, MD: John Hopkins University Press, Baltimore, 3rd edition, 1996.

    Google Scholar 

  15. P. Butler and A. Cantoni, “Eigenvalues and eigenvectors of symmetric centrosymmetric matrices,” Linear Algebra and its Applications, vol. 13, pp. 275288, Mar. 1976.

    Google Scholar 

  16. I. Dologlou and G. Carayannis, “Physical Representation of Signal Reconstruction from Reduced Rank Matrices,” IEEE Trans. Signal Processing, vol. 39, no. 7, pp. 1682–1684, July 1991.

    Article  Google Scholar 

  17. S. Dodo and M. Moonen, “SVD-based optimal filtering with applications to noise reduction in speech signals,” Tech. Rep. ESAT-SISTA/TR 1999–33, ESAT, K.U.Leuven, Belgium, Apr. 1999.

    Google Scholar 

  18. J. Allen and D. Berkley, “Image method for efficiently simulating small-room acoustics,” J. Acoust. Soc. Amer., vol. 65, pp. 943–950, Apr. 1979.

    Article  Google Scholar 

  19. C. C. Paige, “Computing the generalized singular value decomposition,” SIAM J. Sci. Statist. Comput., vol. 7, pp. 1126–1146, 1986.

    Article  MathSciNet  MATH  Google Scholar 

  20. C. Van Loan, “Computing the CS and the Generalized Singular Value Decomposition,” Numer. Math.,, no. 46, pp. 479–491, 1985.

    Article  MathSciNet  MATH  Google Scholar 

  21. J. P. Charlier, M. Vanbegin, and P. Van Dooren, “On efficient implementations of Kogbetliantz’s algorithm for computing the singular value decomposition,” Numer. Math., vol. 52, pp. 279–300, 1988.

    Article  MATH  Google Scholar 

  22. M. Moonen, P. Van Dooren, and J. Vandewalle, “A Singular Value Decomposition Updating Algorithm for Subspace Tracking,” SIAM Journal on Matrix Analysis and Applications, vol. 13, no. 4, pp. 1015–1038, Oct. 1992.

    Article  MathSciNet  MATH  Google Scholar 

  23. M. Moonen, P. Van Dooren, and J. Vandewalle, “A systolic algorithm for QSVD updating,” Signal Processing, vol. 25, pp. 203–213, 1991.

    Article  MATH  Google Scholar 

  24. W. M. Gentleman, “Least squares computation by Givens transformations without square roots,” J. Inst. Math. Appl., vol. 12, pp. 329–336, 1973.

    Article  MathSciNet  MATH  Google Scholar 

  25. M. Moonen, P. Van Dooren, and J. Vandewalle, “A systolic array for SVD updating,” SIAM Journal on Matrix Analysis and Applications, vol. 14, no. 2, pp. 353–371, 1993.

    Article  MathSciNet  MATH  Google Scholar 

  26. L. J. Griffiths and C. W. Jim, “An alternative approach to linearly constrained adaptive beamforming,” IEEE Trans. Antennas Propag., vol. 30, pp. 27–34, Jan. 1982.

    Article  Google Scholar 

  27. K. M. Buckley, “Broad-Band Beamforming and the Generalized Sidelobe Canceller,” IEEE Trans. Acoust., Speech, and Signal Processing, vol. 34, no. 5, pp. 1322–1323, Oct. 1986.

    MathSciNet  Google Scholar 

  28. J. Bitzer, K. U. Simmer, and K.-D. Kammeyer, “Theoretical Noise Reduction Limits of the Generalized Sidelobe Canceller (GSC) for Speech Enhancement,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing (ICASSP), Phoenix, AZ, Phoenix, May 1999, vol. 5, pp. 2965–2968.

    Google Scholar 

  29. D. Van Compernolle, “Switching Adaptive Filters for Enhancing Noisy and Reverberant Speech from Microphone Array Recordings,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing (ICASSP), Albuquerque, USA, Albuquerque, 1990, vol. 2, pp. 833–836.

    Google Scholar 

  30. S. Haykin, Adaptive Filter Theory, Prentice Hall, 3rd edition, 1996.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Katholieke Universiteit Leuven, Leuven, Belgium

    Simon Doclo & Marc Moonen

Authors
  1. Simon Doclo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marc Moonen
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Div. of Eng. and Applied Scciences, Harvard University, 33 Oxford Street, 02138, Cambridge, MA, USA

    Michael Brandstein

  2. Dept. of Electrical Engineering, Imperial College, Exhibition Road, SW7 2AZ, London, GB

    Darren Ward

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Doclo, S., Moonen, M. (2001). GSVD-Based Optimal Filtering for Multi-Microphone Speech Enhancement. In: Brandstein, M., Ward, D. (eds) Microphone Arrays. Digital Signal Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-04619-7_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-04619-7_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-07547-6

  • Online ISBN: 978-3-662-04619-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation