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

Open Access Research Article

Detection-Guided Fast Affine Projection Channel Estimator for Speech Applications

Yan Wu Jennifer1*, John Homer2, Geert Rombouts3 and Marc Moonen3

Author Affiliations

1 Canberra Research Laboratory, National ICT Australia and Research School of Information Science and Engineering, The Australian National University, Canberra ACT 2612, Australia

2 School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane QLD 4072, Australia

3 Departement Elektrotechniek, Katholieke Universiteit Leuven, ESAT/SCD, Kasteelpark Arenberg 10, Heverlee 30001, Belgium

For all author emails, please log on.

EURASIP Journal on Audio, Speech, and Music Processing 2007, 2007:071495  doi:10.1155/2007/71495


The electronic version of this article is the complete one and can be found online at: http://asmp.eurasipjournals.com/content/2007/1/071495


Received: 9 July 2006
Revisions received: 16 November 2006
Accepted: 18 February 2007
Published: 12 April 2007

© 2007 Yan Wu Jennifer 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.

In various adaptive estimation applications, such as acoustic echo cancellation within teleconferencing systems, the input signal is a highly correlated speech. This, in general, leads to extremely slow convergence of the NLMS adaptive FIR estimator. As a result, for such applications, the affine projection algorithm (APA) or the low-complexity version, the fast affine projection (FAP) algorithm, is commonly employed instead of the NLMS algorithm. In such applications, the signal propagation channel may have a relatively low-dimensional impulse response structure, that is, the number m of active or significant taps within the (discrete-time modelled) channel impulse response is much less than the overall tap length n of the channel impulse response. For such cases, we investigate the inclusion of an active-parameter detection-guided concept within the fast affine projection FIR channel estimator. Simulation results indicate that the proposed detection-guided fast affine projection channel estimator has improved convergence speed and has lead to better steady-state performance than the standard fast affine projection channel estimator, especially in the important case of highly correlated speech input signals.

References

  1. K Ozeki, T Umeda, An adaptive filtering algorithm using an orthogonal projection to an affine subspace and its properties. Electronics & Communications in Japan 67(5), 19–27 (1984). PubMed Abstract | Publisher Full Text OpenURL

  2. SL Gay, S Tavathia, The fast affine projection algorithm. Proceedings of the 20th International Conference on Acoustics, Speech, and Signal Processing (ICASSP '95), May 1995, Detroit, Mich, USA 5, 3023–3026

  3. JR Casar-Corredera, J Alcazar-Fernandez, An acoustic echo canceller for teleconference systems. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '86), April 1986, Tokyo, Japan 11, 1317–1320

  4. A Gilloire, J Zurcher, Achieving the control of the acoustic echo in audio terminals. Proceedings of European Signal Processing Conference (EUSIPCO '88), September 1988, Grenoble, France, 491–494

  5. S Makino, S Shimada, Echo control in telecommunicaitons. Journal of the Acoustic Society of Japan 11(6), 309–316 (1990)

  6. J Homer, I Mareels, RR Bitmead, B Wahlberg, A Gustafsson, LMS estimation via structural detection. IEEE Transactions on Signal Processing 46(10), 2651–2663 (1998). Publisher Full Text OpenURL

  7. J Homer, Detection guided NLMS estimation of sparsely parametrized channels. IEEE Transactions on Circuits and Systems II 47(12), 1437–1442 (2000). Publisher Full Text OpenURL

  8. J Homer, I Mareels, C Hoang, Enhanced detection-guided NLMS estimation of sparse FIR-modeled signal channels. IEEE Transactions on Circuits and Systems I 53(8), 1783–1791 (2006)

  9. S Haykin, Adaptive Filter Theory, Prentice Hall Information and System Science Series, 3rd edn. (Prentice-Hall, Upper Saddle River, NJ, USA, 1996)

  10. M Bouchard, Multichannel affine and fast affine projection algorithms for active noise control and acoustic equalization systems. IEEE Transactions on Speech and Audio Processing 11(1), 54–60 (2003). Publisher Full Text OpenURL

  11. SG Sankaran, AA Beex, Convergence behavior of affine projection algorithms. IEEE Transactions on Signal Processing 48(4), 1086–1096 (2000). Publisher Full Text OpenURL

  12. G Rombouts, M Moonen, A sparse block exact affine projection algorithm. IEEE Transactions on Speech and Audio Processing 10(2), 100–108 (2002). Publisher Full Text OpenURL

  13. G Rombouts, M Moonen, A fast exact frequency domain implementation of the exponentially windowed affine projection algorithm. Proceedings of IEEE Adaptive Systems for Signal Processing, Communications, and Control Symposium (AS-SPCC '00), October 2000, Lake Louise, Alta., Canada, 342–346

  14. MR Leadbetter, G Lindgren, H Rootzen, Extremes and Related Properties of Random Sequences and Processes (Springer, New York, NY, USA, 1982)

  15. H Cramer, MR Leadbetter, Stationary and Related Stochastic Srocesses: Sample Function Properties and Their Applications (John Wiley & Sons, New York, NY, USA, 1967)