A Class of Sparseness-Controlled Algorithms for Echo Cancellation

In the context of acoustic echo cancellation (AEC), it is shown that the level of sparseness in acoustic impulse responses can vary greatly in a mobile environment. When the response is strongly sparse, convergence of conventional approaches is poor. Drawing on techniques originally developed for network echo cancellation (NEC), we propose a class of AEC algorithms that can not only work well in both sparse and dispersive circumstances, but also adapt dynamically to the level of sparseness using a new sparseness-controlled approach. Simulation results, using white Gaussian noise (WGN) and speech input signals, show improved performance over existing methods. The proposed algorithms achieve these improvement with only a modest increase in computational complexity.

[1]  Kong-Aik Lee,et al.  Improving convergence of the NLMS algorithm using constrained subband updates , 2004, IEEE Signal Processing Letters.

[2]  Donald L. Duttweiler,et al.  Proportionate normalized least-mean-squares adaptation in echo cancelers , 2000, IEEE Trans. Speech Audio Process..

[3]  Eberhard Hänsler,et al.  The hands-free telephone problem- An annotated bibliography , 1992, Signal Process..

[4]  Milos Doroslovacki,et al.  Improving convergence of the PNLMS algorithm for sparse impulse response identification , 2005, IEEE Signal Processing Letters.

[5]  P. Peterson.  Simulating the response of multiple microphones to a single acoustic source in a reverberant room. , 1986, The Journal of the Acoustical Society of America.

[6]  S.L. Gay,et al.  An efficient, fast converging adaptive filter for network echo cancellation , 1998, Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284).

[7]  Milos Doroslovacki,et al.  Wavelet-Based MPNLMS Adaptive Algorithm for Network Echo Cancellation , 2007, EURASIP J. Audio Speech Music. Process..

[8]  Zeljko Zilic,et al.  Echo cancellation in IP networks , 2002, The 2002 45th Midwest Symposium on Circuits and Systems, 2002. MWSCAS-2002..

[9]  Seyed Mojtaba Atarodi,et al.  A fast converging algorithm for network echo cancellation , 2004, IEEE Signal Processing Letters.

[10]  J. Sanubari,et al.  A new variable step size method for the LMS adaptive filter , 2004, The 2004 IEEE Asia-Pacific Conference on Circuits and Systems, 2004. Proceedings..

[11]  Gary W. Elko,et al.  ROOM IMPULSE RESPONSE VARIATION DUE TO THERMAL FLUCTUATION AND ITS IMPACT ON ACOUSTIC ECHO CANCELLATION , 2003 .

[12]  H. Sabine.  Room Acoustics , 1953, The SAGE Encyclopedia of Human Communication Sciences and Disorders.

[13]  W. Kenneth Jenkins,et al.  Low-complexity data reusing methods in adaptive filtering , 2004, IEEE Transactions on Signal Processing.

[14]  Andy W. H. Khong,et al.  Efficient Use Of Sparse Adaptive Filters , 2006, 2006 Fortieth Asilomar Conference on Signals, Systems and Computers.

[15]  Milos Doroslovacki,et al.  Proportionate adaptive algorithms for network echo cancellation , 2006, IEEE Transactions on Signal Processing.

[16]  C.F.N. Cowan,et al.  The convex variable step-size (CVSS) algorithm , 2000, IEEE Signal Processing Letters.

[17]  W. K. Jenkins,et al.  New data-reusing LMS algorithms for improved convergence , 1993, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers.

[18]  Andy W. H. Khong,et al.  Affine projection and recursive least squares adaptive filters employing partial updates , 2004, Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004..

[19]  Patrick A. Naylor,et al.  Proportionate Frequency Domain Adaptive Algorithms for Blind Channel Identification , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[20]  Steven L. Grant,et al.  A new multi-algorithm approach to sparse system adaptation , 2005, 2005 13th European Signal Processing Conference.

[21]  Patrick A. Naylor,et al.  Selective-tap adaptive algorithms in the solution of the nonuniqueness problem for stereophonic acoustic echo cancellation , 2005, IEEE Signal Processing Letters.

[22]  Jacob Benesty,et al.  Stereophonic acoustic echo cancellation: analysis of the misalignment in the frequency domain , 2006, IEEE Signal Processing Letters.

[23]  Kevin T. Wagner,et al.  Towards analytical convergence analysis of proportionate-type nlms algorithms , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[24]  Jont B. Allen,et al.  Image method for efficiently simulating small‐room acoustics , 1976 .

[25]  Gerhard Schmidt,et al.  Adaptive Algorithms for the Identification of Sparse Impulse Responses , 2006 .