Skip to main content
SpringerLink
Log in

Adaptive Blind Multichannel Identification

  • Chapter
Springer Handbook of Speech Processing

Part of the book series: Springer Handbooks ((SHB))

Abstract

Blind multichannel identification was first introduced in the mid 1970s and initially studied in the communication society with the intention of designing more-efficient communication systems by avoiding a training phase. Recently this idea has become increasingly interesting for acoustics and speech processing research, driven by the fact that in most acoustic applications for speech processing and communication very little or nothing is known about the source signals. Since human ears have an extremely wide dynamic range and are much more sensitive to weak tails of the acoustic impulse responses, these impulse responses need to be modeled using fairly long filters. Attempting to identify such a multichannel system blindly with a batch method involves intensive computational complexity. This is not just bad system design, but technically rather implausible, particularly for real-time systems. Therefore, adaptive blind multichannel identification algorithms are favorable and pragmatically useful. This chapter describes some fundamental issues in blind multichannel identification and reviews a number of state-of-the-art adaptive algorithms.

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 579.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 729.00
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

Abbreviations

CR:

cross-relation

DFT:

discrete Fourier transform

FFT:

fast Fourier transform

FIR:

finite impulse response

HOS:

higher-order statistics

LMS:

least mean square

MCN:

multichannel Newton

MIMO:

multiple-input multiple-output

MSE:

mean-square error

PNLMS:

proportionate NLMS

SIMO:

single-input multiple-output

SISO:

single-input single-output

SOS:

second-order statistics

References

  1. Y. Sato: A method of self-recovering equalization for multilevel amplitude-modulation, IEEE Trans. Commun. COM-23, 679-682 (1975)

    Article  Google Scholar 

  2. D.N. Godard: Self-recovering equalization and carrier tracking in two-dimensional data communication systems, IEEE Trans. Commun. COM-28, 1867-1875 (1980)

    Article  Google Scholar 

  3. J.R. Treichler, B.G. Agee: A new approach to multipath correction of constant modulus signals, IEEE Trans. Acoust. Speech ASSP-31, 459-472 (1983)

    Article  Google Scholar 

  4. A. Benveniste, M. Goursat: Blind equalizers, IEEE Trans. Commun. COM-32, 871-883 (1984)

    Article  MATH  Google Scholar 

  5. J.M. Mendel: Tutorial on higher-order statistics (spectra) in signal processing and system theory: theoretical results and some applications, Proc. IEEE 79, 278-305 (1991)

    Article  Google Scholar 

  6. L. Tong, G. Xu, T. Kailath: A new approach to blind identification and equalization of multipath channels, Proc. 25th Asilomar Conf. on Signals, Systems, and Computers, Vol. 2 (1991) pp. 856-860

    Google Scholar 

  7. H. Liu, G. Xu, L. Tong: A deterministic approach to blind equalization, Proc. 27th Asilomar Conf. on Signals, Systems, and Computers, Vol. 1 (1993) pp. 751-755

    Google Scholar 

  8. M.I. Gürelli, C.L. Nikias: A new eigenvector-based algorithm for multichannel blind deconvolution of input clolored signals, Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., Vol. 4 (1993) pp. 448-451

    Google Scholar 

  9. L.A. Baccala, S. Roy: A new blind time-domain channel identification method based on cyclostationarity, IEEE Signal Process. Lett. 1, 89-91 (1994)

    Article  Google Scholar 

  10. G. Xu, H. Liu, L. Tong, T. Kailath: A least-squares approach to blind channel identification, IEEE Trans. Signal Process. 43, 2982-2993 (1995)

    Article  Google Scholar 

  11. E. Moulines, P. Duhamel, J.F. Cardoso, S. Mayrargue: Subspace methods for the blind identification of multichannel FIR filters, IEEE Trans. Signal Process. 43, 516-525 (1995)

    Article  Google Scholar 

  12. D. Slock: Blind fractionally-spaced equalization, prefect reconstruction filerbanks, and multilinear prediction, Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., Vol. 4 (1994) pp. 585-588

    Google Scholar 

  13. Y. Hua: Fast maximum likelihood for blind identification of multiple FIR channels, IEEE Trans. Signal Process. 44, 661-672 (1996)

    Article  Google Scholar 

  14. L. Tong, S. Perreau: Multichannel blind identification: from subspace to maximum likelihood methods, Proc. IEEE 86, 1951-1968 (1998)

    Article  Google Scholar 

  15. Y. Huang, J. Benesty: Adaptive multi-channel least mean square and Newton algorithms for blind channel identification, Signal Process. 82, 1127-1138 (2002)

    Article  MATH  Google Scholar 

  16. C. Avendano, J. Benesty, D.R. Morgan: A least squares component normalization approach to blind channel identification, Proc. IEEE Int. Conf. Acoust. Speech Signal Process., Vol. 4 (1999) pp. 1797-1800

    Google Scholar 

  17. S. Haykin: Adaptive Filter Theory, 4th edn. (Prentice Hall, Upper Saddle River 2002)

    MATH  Google Scholar 

  18. T.K. Moon, W.C. Stirling: Mathematical Methods and Algorithms (Prentice Hall, Upper Saddle River 1999)

    Google Scholar 

  19. H. Chen, X. Cao, J. Zhu: Convergence of stochastic-approximation-based algorithms for blind channel identification, IEEE Trans. Inform. Theory 48, 1214-1225 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  20. Y. Huang, J. Benesty, J. Chen: Optimal step size of the adaptive multichannel LMS algorithm for blind SIMO identification, IEEE Signal Process. Lett. 12, 173-176 (2005)

    Article  Google Scholar 

  21. M. Dentino, J. McCool, B. Widrow: Adaptive filtering in the frequency domain, Proc. IEEE 66, 1658-1659 (1978)

    Article  Google Scholar 

  22. A.V. Oppenheim, R.W. Schafer: Discrete-Time Signal Processing (Prentice Hall, Englewood Cliffs 1989)

    MATH  Google Scholar 

  23. D.H. Brandwood: A complex gradient operator and its application in adaptive array theory, Proc. IEE 130, 11-16 (1983), Pts. F and H

    MathSciNet  Google Scholar 

  24. Y. Huang, J. Benesty: A class of frequency-domain adaptive approaches to blind multichannel identification, IEEE Trans. Signal Process. 51, 11-24 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  25. J. Benesty, D. Morgan: Frequency-domain adaptive filtering revisited, generalization to the multi-channel case, and application to acoustic echo cancellation, Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., Vol. 2 (2000) pp. 789-792

    Google Scholar 

  26. D.L. Duttweiler: Proportionate normalized least-mean-square adaptation in echo cancelers, IEEE Trans. Speech Audio Process. 8, 508-518 (2000)

    Article  Google Scholar 

  27. J. Benesty, Y. Huang: The LMS, PNLMS, and exponentiated gradient algorithms, Proc. EUSIPCO (2004) pp. 721-724

    Google Scholar 

  28. J. Kivinen, M.K. Warmuth: Exponentiated gradient versus gradient descent for linear predictors, Inform. Comput. 132, 1-64 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  29. S.I. Hill, R.C. Williamson: Convergence of exponentiated gradient algorithms, IEEE Trans. Signal Process. 49, 1208-1215 (2001)

    Article  Google Scholar 

  30. J. Benesty, Y. Huang, J. Chen: An exponentiated gradient adaptive algorithm for blind identification of sparse SIMO systems, Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., Vol. 2 (2004) pp. 829-832

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Alcatel-Lucent, Bell Laboratories, 600 Mountain Avenue, 07974, Murray Hill, NJ, USA

    Yiteng (Arden) Huang Dr.

  2. INRS-EMT, University of Quebec, 800 de la Gauchetiere Ouest, H5A 1K6, Montreal, Quebec, Canada

    Jacob Benesty Prof.

  3. Alcatel-Lucent, Bell Laboratories, 600 Mountain Ave, 07974, Murray Hill, NJ, USA

    Jingdong Chen Dr.

Authors
  1. Yiteng (Arden) Huang Dr.
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jacob Benesty Prof.
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jingdong Chen Dr.
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Yiteng (Arden) Huang Dr. , Jacob Benesty Prof. or Jingdong Chen Dr. .

Editor information

Editors and Affiliations

  1. INRS-EMT, University of Quebec, 800 de la Gauchetiere Ouest, H5A 1K6, Montreal, Quebec, Canada

    Jacob Benesty Dr.

  2. Avayalabs Research, 233 Mount Airy Road, 07920, Basking Ridge, NJ, USA

    M. Mohan Sondhi Ph.D.

  3. Alcatel-Lucent, Bell Laboratories, 600 Mountain Avenue, 07974, Murray Hill, NJ, USA

    Yiteng Arden Huang Dr.

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Huang, Y.(., Benesty, J., Chen, J. (2008). Adaptive Blind Multichannel Identification. In: Benesty, J., Sondhi, M.M., Huang, Y.A. (eds) Springer Handbook of Speech Processing. Springer Handbooks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-49127-9_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-49127-9_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-49125-5

  • Online ISBN: 978-3-540-49127-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation