Computational Complexity of Adaptive Algorithms in Echo Cancellation

International Journal of Electronics and Communication Engineering

© 2015 by SSRG - IJECE Journal

Volume 2 Issue 7

Year of Publication : 2015

Authors : Mrs. A.P.Patil and Dr.Mrs.M.R.Patil

10.14445/23488549/IJECE-V2I7P104

How to Cite?

Mrs. A.P.Patil and Dr.Mrs.M.R.Patil, "Computational Complexity of Adaptive Algorithms in Echo Cancellation," SSRG International Journal of Electronics and Communication Engineering, vol. 2,  no. 7, pp. 11-15, 2015. Crossref, https://doi.org/10.14445/23488549/IJECE-V2I7P104

Abstract:

The proportionate normalized leastmean-squares (PNLMS) algorithm is a new scheme for echo canceller adaptation. On typical echo paths, the proportionate normalized least-mean-squares (PNLMS) adaptation algorithm converges significantly faster than the normalized least-mean-squares (NLMS) algorithm. Two proportionate affine projection sign algorithms (APSAs) are also proposed for network echo cancellation application where the impulse response is often real-valued with sparse coefficients and long filter length. The proportionate- type algorithms can achieve fast convergence rate in sparse impulse responses and low steady-state misalignment. The new algorithms are more robust to impulsive interferences and colored input signals than the proportionate least mean squares algorithm, normalized sign algorithm and the robust proportionate affine projection algorithm. The computational complexity of the new algorithms is lower than the affine projection algorithm (APA) family due to the elimination of the matrix inversion. The computational complexity of the proportionate APSAs are compared with that of conventional algorithms in terms of the total number of additions, multiplications, divisions, comparisons, and square-roots.

Keywords:

Adaptive filter, Proportionate filters, Proportionate adaptive algorithm, Sparse channel identification

References:

[1] J.  Benesty and S. L.  Gay, “An improved PNLMS algorithm,” in Proc. IEEE  Int. Conf.  Acoust., Speech, Signal Process. ICASSP’02, 2002, vol. 2, pp. 1881–1884.
[2] P. Loganathan, A. Khong, and P. Naylor, “A class of sparseness-controlled algorithms for echo cancellation,” IEEE Trans. Audio, Speech, Lang. Process., vol. 17, no. 8, pp. 1591–1601, Nov. 2009.
[3] J. Arenas-Garcia and A. Figueiras-Vidal, “Adaptive combination of proportionate filters for sparse echo cancellation,” IEEE Trans. Audio, Speech, Lang. Process., vol. 17, no. 6, pp. 1087–1098, Aug. 2009.
[4] H. Deng and M. Doroslovacki, “Improving convergence of the PNLMS algorithm for sparse impulse response identification,” IEEE Signal Process. Lett., vol. 12, no. 3, pp. 181–184, Mar. 2005.
[5] K. Sakhnov, “An improved proportionate affine projection algorithm for network echo cancellation,” in Proc. Int. Conf. Syst., Signals Image Process. (IWSSIP 2008), 2008, pp. 125–128.
[6] C. Paleologu, S. Ciochina, and J. Benesty, “An efficient proportionate affine projection algorithm for echo cancellation,” IEEE Signal Process. Lett., vol. 17, no. 2, pp. 165–168, Feb. 2010.
[7] Zengli Yang, Yahong Rosa Zheng,, and Steven L. Grant, “Proportionate Affine Projection sign algorithm for network echo cancellation,” IEEE Trans on Audio,Speech, and Language Process, vol. 19, no. 8, november 2011 .
[8] L. R. Vega, H. Rey, J. Benesty, and S. Tressens, “A new robust variable step-size NLMS algorithm,” IEEE Trans. Signal Process., vol. 56, no. 5, pp. 1878–1893, May 2008.
[9] H.-C. Shin and A. Sayed, “Mean-square performance of a family of affine projection algorithms,” IEEE Trans. Signal Process., vol. 52, no.1, pp. 90–102, Jan. 2004.
[10] M. A. M. Nekuii, “A fast converging algorithm for network echo cancellation,”IEEE Signal Process. Lett., vol. 11, no. 4, pp. 427–430, Apr.2004.
[11] H. Deng and M. Doroslovacki, “Improving convergence of the PNLMS algorithm for sparse impulse response identification,” IEEE Signal Process. Lett., vol. 12, no. 3, pp. 181–184, Mar. 2005.
[12] H. Deng and M. Doroslovacki, “Proportionate adaptive algorithms for network echo cancellation,” IEEE Trans. Signal Process., vol. 54, no.5, pp. 1794–1803, May 2006.
[13] Hu Chun Feng ,Zhao Zhengang and Wang DeJun,” On the Algorithm and Implementationof an Acoustic Echo Canceller,” Proceedings of ICSP ’ 1996
[14] Fredric Lindström,  Christian Schüldt, and Ingvar Claesson,”An Improvement of the Two-Path Algorithm Transfer Logic for Acoustic Echo Cancellation ,”IEEE Trans. Audio, Speech, And Language Processing, Vol. 15, No. 4, May 2007.
[15] K. Mayyas,” Performance analysis of Deficient Length LMS Adaptive algorithm”IEEE Transactions On Signal Processing, Vol. 53, No. 8, August 2005
[16] Arian Maleki,and Kambiz Nayebi, “A New Efficient PNLMS Based Algorithm for Adaptive Line Echo Cancellation”, IEEE Trans. Signal Processing, 2003.
[17] D. L. Duttweiler, “ Proportionate normalized least mean square adaptation in echo cancellers”, IEEE Trans. Speech Audio Processing, Vol.8,Sept.2000.
[18] Abhishek Tandon, Ahmad, and Swamy, “ An Efficient, low complexity,normalized LMS algorithm for Echo Cancellation”, IEEE Trans. Signal Processing, 2004.