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This article is part of the series Adaptive Partial-Update and Sparse System Identification.

Open Access Research Article

A Low Delay and Fast Converging Improved Proportionate Algorithm for Sparse System Identification

Andy WH Khong1*, Patrick A Naylor1 and Jacob Benesty2

Author Affiliations

1 Department of Electrical and Electronic Engineering, Imperial College London, Exhibition Road, London SW7 2AZ, UK

2 INRS-EMT, Université du Québec, Suite 6900, 800 de la Gauchetière Ouest, Montréal, QC H5A 1K6, Canada

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EURASIP Journal on Audio, Speech, and Music Processing 2007, 2007:084376  doi:10.1155/2007/84376

The electronic version of this article is the complete one and can be found online at: http://asmp.eurasipjournals.com/content/2007/1/084376


Received:4 July 2006
Revisions received:1 December 2006
Accepted:24 January 2007
Published:3 April 2007

© 2007 Andy W. H. Khong et al.

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.

A sparse system identification algorithm for network echo cancellation is presented. This new approach exploits both the fast convergence of the improved proportionate normalized least mean square (IPNLMS) algorithm and the efficient implementation of the multidelay adaptive filtering (MDF) algorithm inheriting the beneficial properties of both. The proposed IPMDF algorithm is evaluated using impulse responses with various degrees of sparseness. Simulation results are also presented for both speech and white Gaussian noise input sequences. It has been shown that the IPMDF algorithm outperforms the MDF and IPNLMS algorithms for both sparse and dispersive echo path impulse responses. Computational complexity of the proposed algorithm is also discussed.

References

  1. J Radecki, Z Zilic, K Radecka, Echo cancellation in IP networks. Proceedings of the 45th Midwest Symposium on Circuits and Systems, August 2002, Tulsa, Okla, USA 2, 219–222

  2. M Boujida, J-M Boucher, Higher order statistics applied to wavelet identification of marine seismic signals. Proceedings of European Signal Processing Conference (EUSIPCO '96), September 1996, Trieste, Italy

  3. Y-F Cheng, DM Etter, Analysis of an adaptive technique for modeling sparse systems. IEEE Transactions on Acoustics, Speech, and Signal Processing 37(2), 254–264 (1989). Publisher Full Text OpenURL

  4. EA Robinson, TS Durrani, Geophysical Signal Processing (Prentice-Hall, Englewood Cliffs, NJ, USA, 1986)

  5. DL Duttweiler, Proportionate normalized least-mean-squares adaptation in echo cancelers. IEEE Transactions on Speech and Audio Processing 8(5), 508–518 (2000). Publisher Full Text OpenURL

  6. J Benesty, SL Gay, An improved PNLMS algorithm. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '02), May 2002, Orlando, Fla, USA 2, 1881–1884

  7. J Cui, PA Naylor, DT Brown, An improved IPNLMS algortihm for echo cancellation in packet-switched networks. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '04), May 2004, Montreal, Que, Canada 4, 141–144

  8. H Deng, M Doroslovački, New sparse adaptive algorithms using partial update. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '04), May 2004, Montreal, Que, Canada 2, 845–848

  9. K Dogançay, O Tanrikulu, Adaptive filtering algorithms with selective partial updates. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 48(8), 762–769 (2001). Publisher Full Text OpenURL

  10. H Deng, M Doroslovački, Improving convergence of the PNLMS algorithm for sparse impulse response identification. IEEE Signal Processing Letters 12(3), 181–184 (2005)

  11. JW Cooley, JW Tukey, An algorithm for the machine calculation of complex Fourier series. Mathematics of Computation 19(90), 297–301 (1965). Publisher Full Text OpenURL

  12. S Haykin, Adaptive Filter Theory, Information and System Science Series, h (Prentice-Hall, Englewood Cliffs, NJ, USA, 2002)

  13. JJ Shynk, Frequency-domain and multirate adaptive filtering. IEEE Signal Processing Magazine 9(1), 14–37 (1992). Publisher Full Text OpenURL

  14. E Hänsler, GU Schmidt, Hands-free telephones - joint control of echo cancellation and postfiltering. Signal Processing 80(11), 2295–2305 (2000). Publisher Full Text OpenURL

  15. J-S Soo, KK Pang, Multidelay block frequency domain adaptive filter. IEEE Transactions on Acoustics, Speech, and Signal Processing 38(2), 373–376 (1990). Publisher Full Text OpenURL

  16. J Benesty, T Gänsler, DR Morgan, MM Sondhi, SL Gay, Advances in Network and Acoustic Echo Cancellation (Springer, New York, NY, USA, 2001)

  17. AWH Khong, J Benesty, PA Naylor, An improved proportionate multi-delay block adaptive filter for packet-switched network echo cancellation. Proceedings of the 13th European Signal Processing Conference (EUSIPCO '05), September 2005, Antalya, Turkey

  18. J Benesty, YA Huang, J Chen, PA Naylor, Adaptive algorithms for the identification of sparse impulse responses. in Selected Methods for Acoustic Echo and Noise Control, ed. by Hänsler E, Schmidt G (Springer, New York, NY, USA, 2006), pp. 125–153 chapter 5 OpenURL

  19. PO Hoyer, Non-negative matrix factorization with sparseness constraints. Journal of Machine Learning Research 5, 1457–1469 (2004)

  20. R Gray, On the asymptotic eigenvalue distribution of toeplitz matrices. IEEE Transactions on Information Theory 18(6), 725–730 (1972). Publisher Full Text OpenURL

  21. J Lee, S-C Chong, On the convergence properties of multidelay frequency domain adaptive filter. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '99), March 1999, Phoenix, Ariz, USA 4, 1865–1868