Open Access

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

EURASIP Journal on Audio, Speech, and Music Processing20072007:084376

DOI: 10.1155/2007/84376

Received: 4 July 2006

Accepted: 24 January 2007

Published: 3 April 2007


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.


Authors’ Affiliations

Department of Electrical and Electronic Engineering, Imperial College London
INRS-EMT, Université du Québec


  1. Radecki J, Zilic Z, Radecka K: Echo cancellation in IP networks. Proceedings of the 45th Midwest Symposium on Circuits and Systems, August 2002, Tulsa, Okla, USA 2: 219-222.Google Scholar
  2. Boujida M, Boucher J-M: Higher order statistics applied to wavelet identification of marine seismic signals. Proceedings of European Signal Processing Conference (EUSIPCO '96), September 1996, Trieste, ItalyGoogle Scholar
  3. Cheng Y-F, Etter DM: Analysis of an adaptive technique for modeling sparse systems. IEEE Transactions on Acoustics, Speech, and Signal Processing 1989,37(2):254-264. 10.1109/29.21688View ArticleGoogle Scholar
  4. Robinson EA, Durrani TS: Geophysical Signal Processing. Prentice-Hall, Englewood Cliffs, NJ, USA; 1986.Google Scholar
  5. Duttweiler DL: Proportionate normalized least-mean-squares adaptation in echo cancelers. IEEE Transactions on Speech and Audio Processing 2000,8(5):508-518. 10.1109/89.861368View ArticleGoogle Scholar
  6. Benesty J, Gay SL: 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.Google Scholar
  7. Cui J, Naylor PA, Brown DT: 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.Google Scholar
  8. Deng H, Doroslovački M: 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.Google Scholar
  9. 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. 10.1109/82.959866View ArticleMATHGoogle Scholar
  10. Deng H, Doroslovački M: Improving convergence of the PNLMS algorithm for sparse impulse response identification. IEEE Signal Processing Letters 2005,12(3):181-184.View ArticleGoogle Scholar
  11. Cooley JW, Tukey JW: An algorithm for the machine calculation of complex Fourier series. Mathematics of Computation 1965,19(90):297-301. 10.1090/S0025-5718-1965-0178586-1MathSciNetView ArticleMATHGoogle Scholar
  12. Haykin S: Adaptive Filter Theory, Information and System Science Series. 4th edition. Prentice-Hall, Englewood Cliffs, NJ, USA; 2002.Google Scholar
  13. Shynk JJ: Frequency-domain and multirate adaptive filtering. IEEE Signal Processing Magazine 1992,9(1):14-37. 10.1109/79.109205View ArticleGoogle Scholar
  14. Hänsler E, Schmidt GU: Hands-free telephones - joint control of echo cancellation and postfiltering. Signal Processing 2000,80(11):2295-2305. 10.1016/S0165-1684(00)00118-3View ArticleMATHGoogle Scholar
  15. Soo J-S, Pang KK: Multidelay block frequency domain adaptive filter. IEEE Transactions on Acoustics, Speech, and Signal Processing 1990,38(2):373-376. 10.1109/29.103078View ArticleGoogle Scholar
  16. Benesty J, Gänsler T, Morgan DR, Sondhi MM, Gay SL: Advances in Network and Acoustic Echo Cancellation. Springer, New York, NY, USA; 2001.View ArticleMATHGoogle Scholar
  17. Khong AWH, Benesty J, Naylor PA: 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, TurkeyGoogle Scholar
  18. Benesty J, Huang YA, Chen J, Naylor PA: Adaptive algorithms for the identification of sparse impulse responses. In Selected Methods for Acoustic Echo and Noise Control. Edited by: Hänsler E, Schmidt G. Springer, New York, NY, USA; 2006:125-153. chapter 5Google Scholar
  19. Hoyer PO: Non-negative matrix factorization with sparseness constraints. Journal of Machine Learning Research 2004, 5: 1457-1469.MathSciNetMATHGoogle Scholar
  20. Gray R: On the asymptotic eigenvalue distribution of toeplitz matrices. IEEE Transactions on Information Theory 1972,18(6):725-730. 10.1109/TIT.1972.1054924View ArticleMATHGoogle Scholar
  21. Lee J, Chong S-C: 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.Google Scholar


© Andy W.H. Khong 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.