Survey of Sparse Adaptive Filters for Acoustic Echo Cancellation

Full Text (PDF, 1217KB), PP.16-24

Views: 0 Downloads: 0

Author(s)

Krishna Samalla 1,* G. Mallikarjuna Rao 2 Ch.Stayanarayana 1

1. Department of Computer Science and Engineering Jawaharlal Nehru Technological University Kakinada, Andhra Pradesh, India

2. DRDO (RCI), Andhra Pradesh, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2013.01.03

Received: 1 Oct. 2012 / Revised: 1 Nov. 2012 / Accepted: 29 Nov. 2012 / Published: 8 Jan. 2013

Index Terms

Network and Acoustic echo cancellation, Adaptive filter, Sparseness measure, NLMS, VSS-NLMS, PNLMS, IPNLMS

Abstract

This paper reviews the existing developments of adaptive methods of sparse adaptive filters for the identification of sparse impulse response in both network and acoustic echo cancellation from the last decade. A variety of different architectures and novel training algorithms have been proposed in literature. At present most of the work in echo cancellation on using more than one method. Sparse adaptive filters take the advantage of each method and showing good improvement in the sparseness measure performance. This survey gives an overview of existing sparse adaptive filters mechanisms and discusses their advantages over the traditional adaptive filters developed for echo cancellation.

Cite This Paper

Krishna Samalla,G.Mallikarjuna Rao,Ch.Stayanarayana,"Survey of Sparse Adaptive Filters for Acoustic Echo Cancellation", IJIGSP, vol.5, no.1, pp.16-24, 2013. DOI: 10.5815/ijigsp.2013.01.03

Reference

[1]J. Radecki, Z. Zilic, and K. Radecka, “Echo cancellation in IPnetworks,” in Proceedings of the 45th Midwest Symposium oCircuits and Systems, vol. 2, pp. 219–222, Tulsa, Okla, USA,August 2002.

[2]R. H. Kwong and E. Johston, “A variable step-size algorithm for adaptivefiltering,” IEEE Trans. Signal processing, vol. 40, pp. 1633–1642, 1992.

[3]C. Rusu and F. N. Cowan, “The convex variable step size (CVSS) algorithm,” IEEE Signal processing Letter, vol. 7, pp. 256–258, 2000.

[4]J. Sanubari, “A new variable step size method for the LMS adaptive filter,” in IEEE Asia-Pacific Conference on Circuits and systems, 2004.

[5]A.W. H. Khong and P. A. Naylor, “Selective-tap adaptive algorithms in the solution of the non-uniqueness problem for stereophonic acousticecho cancellation,” IEEE Signal Processing Lett., vol. 12, no. 4, pp.269–272, Apr. 2005.

[6]P. A. Naylor and A. W. H. Khong, “Affine projection and recursive least squares adaptive filters employing partial updates,” in Proc. Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, vol. 1,Nov. 2004, pp. 950–954.

[7]K. A. Lee and S. Gan, “Improving convergence of the NLMS algorithm using constrained subbands updates,” IEEE Signal Processing Lett.,vol. 11, no. 9, pp. 736–739, Sept. 2004.

[8]D. L. Duttweiler, “Proportionate normalized least mean square adaptation in echo cancellers,” IEEE Trans. Speech Audio Processing, vol. 8,no. 5, pp. 508–518, Sep. 2000.

[9]M. M. Sondhi, “An adaptive echo canceller,” Bell Syst.Tech. J., vol. XLVI-3, pp. 497–510, Mar. 1967

[10]J. Benesty, T. Gänsler, D. R. Morgan, M. M. Sondhi, and S. L. Gay, Advances in Network and Acoustic Echo Cancellation. Berlin, Germany: Springer-Verlag, 2001

[11]S. Haykin, Adaptive Filter Theory. Fourth Edition, Upper Saddle River, NJ: Prentice-Hall,2002.

[12]K.Ozeki and T. Umeda, “An adaptive filtering algorithm using an orthogonal projection to an affine subspace and its properties,” Electron. Commun. Jpn., vol. 67-A, pp. 19–27, May 1984.

[13]S. L. Gay and S.Tavathia, “The fast affine projection algorithm,” in Proc. IEEE ICASSP, 1995, vol. 5, pp. 3023–3026.

[14]M. Tanaka, Y. Kaneda, S. Makino, and J. Kojima, “A fast projection algorithm for adaptive filtering,” IEICE Trans. Fundamentals, vol. E78-A, pp. 1355–1361, Oct. 1995.

[15]Sparse Adaptive Filters for Echo Cancellation Constantin Paleologu, Jacob Benesty, and Silviu Ciochin˘a 2010.

[16]J. Homer, I. Mareels, R. R. Bitmead, B. Wahlberg, and A. Gustafsson, “LMS estimation via structural detection,” IEEE Trans. Signal Processing, vol. 46, pp. 2651–2663, Oct. 1998.

[17]S. Makino, Y. Kaneda, and N. Koizumi, “Exponentially weighted step-size NLMS adaptive filter based on the statistics of a room impulse response,” IEEE Trans. Speech, Audio Processing.

[18]A. Sugiyama, H. Sato,A. Hirano, and S. Ikeda, “A fast convergence algorithm for adaptive FIR filters under computational constraint for adaptive tap-position control,” IEEE Trans. Circuits Syst. II, vol. 43, pp. 629–636, Sept. 1996.

[19]D. L.Duttweiler, “Proportionate normalized least-mean-squares adaptation in echo cancelers,” IEEETrans. Speech,Audio Processing, vol. 8, pp. 508–518, Sept. 2000.

[20]J. Benesty and S. L. Gay, “An improved PNLMS algorithm,” in Proc. IEEE ICASSP, 2002, pp.1881–1884

[21]J. Kivinen and M. K. Warmuth, “Exponentiated gradient versus gradient descent for linearpredictors,” Inform. Comput., vol. 132, pp. 1–64, Jan. 1997.

[22]H. Deng and M. Doroslovaˇcki, “Improving convergence of the PNLMS algorithm for sparse impulse response identification,” IEEE Signal Processing Lett., vol. 12, pp. 181–184, Mar. 2005.

[23]H. Deng and M. Doroslovaˇcki, “Proportionate adaptive algorithms for network echo cancellation,” IEEE Trans. Signal Processing, vol. 54, pp. 1794–1803, May 2006.

[24]J. Homer, I. Mareels, R. R. Bitmead, B. Wahlberg, and A. Gustafsson, “LMS estimation via structural detection,” IEEE Trans. Signal Processing, vol. 46, pp. 2651–2663, Oct. 1998.

[25]B. D. Rao and B. Song, “Adaptive filtering algorithms for promoting sparsity,” ICASSP, pp. 361-364, Jun. 2003.

[26]Jie Yang, Xiaoming Zhu, Gerald E. Sobelman and Keshab K. Parhi, "Sparseness-Controlled Adaptive Tap Algorithms for Partial Update Adaptive Filters," Proceedings, 7th International Conference on Information, Communications and Signal Processing, 2009

[27]J. Arenas-Garc´ıa and A. R. Figueiras-Vidal, “Adaptive combination of proportionate filters for sparse echo cancellation,“IEEE Trans. Audio, Speech, Lang. Process., vol. 17, pp. 1087–1098, Aug. 2009

[28]Adaptive algorithms for sparse echo cancellation Patrick A. Naylor Jingjing Cui, Mike Brookes www.elsevier.com/locate/sigpro Signal Processing 86 (2006) 1182–1192

[30]Sparse Signal Processing Using iterative Method with Adaptive Thresholding (IMA T) F Marvasti, M Azghani and P Imani, Pakrouh SJ Heydari and A Golmohammadi, A Kazerouni, MMKhalili 978-1-4673-0747-5/12/$31.00 ©2012 IEEE.

[31]Performance Analysis of Norm Constraint Least l0 Mean Square algorithm guolong Su, Jian Jin, Yuantao Gu, Member, IEEE, and Jian Wang IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 60, NO. 5, MAY 2012.

[32]Pradeep Loganathan, et al, A Class Of Sparseness-ControlledAlgorithms For Echo Cancellation, IEEE Transactions OnAudio, Speech, And Language Processing, Vol. 17, No. 8, November 2009

[33]A new variable step size lms adaptive filtering Algorithm, AO Wei, XIANG Wan-Qin, ZHANG You-Peng, WANG Lei, 978-0-7695-4647-6/12 © 2012 IEEE DOI 10.1109/ICCSEE.2012.115

[34]Francisco das Chagas de Souza, Rui Seara, Dennis R. Morgan: An Enhanced IAF-PNLMS Adaptive Algorithm for Sparse Impulse Response Identification. IEEE Transactions on Signal Processing 60(6): 3301-3307 (2012)

[35]P. Loganathan, W. H. A. Khong, and P. A. Naylor, “A sparseness controlled proportionate algorithm for acoustic echo cancellation,” in Proc. EuropeanSignal Processing Conference, 2008.

[36]K. Dogancay and P. A. Naylor, “Recent advances in partial update and sparsea daptive filters,” in Proc. European Signal Processing Conference, 2005