Sparse adaptive algorithm based on different update probability for Acoustic Echo Cancellation

A novel adaptation strategy is proposed for Acoustic Echo Cancellation (AEC). The new algorithm firstly partitions the adaptive filter into several blocks and the successive blocks with the maximum h norm are considered to be the active blocks. Then the coefficients of the active blocks are adapted with large update probability to ensure the identification accuracy while the zero coefficients are adapted with small update probability to decrease the number of adapting coefficients. By excluding most of the zero coefficients from adaptation, the new algorithm improves its performance in terms of convergence and computation. The simulation performed in the context of AEC demonstrates the robustness and the advantages of the new algorithm.

[1]  Yonggang Zhang,et al.  A New Variable Tap-Length LMS Algorithm to Model an Exponential Decay Impulse Response , 2007, IEEE Signal Processing Letters.

[2]  Yonggang Zhang,et al.  An improved variable tap-length LMS algorithm , 2009, Signal Process..

[3]  Yuantao Gu,et al.  Robust zero-point attraction least mean square algorithm on near sparse system identification , 2013, IET Signal Process..

[4]  Dajun Sun,et al.  A block parallel ℓ0-norm penalized shrinkage and widely linear affine projection algorithm for adaptive filter , 2017, China Communications.

[5]  Yuantao Gu,et al.  $l_{0}$ Norm Constraint LMS Algorithm for Sparse System Identification , 2009, IEEE Signal Processing Letters.

[6]  Young-Cheol Park,et al.  Nonlinear Acoustic Echo Cancellation Using a Nonlinear Postprocessor With a Linearly Constrained Affine Projection Algorithm , 2015, IEEE Transactions on Circuits and Systems II: Express Briefs.

[7]  Sheng Zhang,et al.  Robust Variable Step-Size Decorrelation Normalized Least-Mean-Square Algorithm and its Application to Acoustic Echo Cancellation , 2016, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[8]  José Carlos M. Bermudez,et al.  Identification of sparse impulse responses - design and implementation using the partial Haar block wavelet transform , 2012, Digit. Signal Process..

[9]  Jianming Liu,et al.  Proportionate Adaptive Filtering for Block-Sparse System Identification , 2015, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[10]  F. Y. Wu,et al.  Non-Uniform Norm Constraint LMS Algorithm for Sparse System Identification , 2013, IEEE Communications Letters.

[11]  Ying Wang,et al.  Variable regularisation efficient μ-law improved proportionate affine projection algorithm for sparse system identification , 2012 .

[12]  Eduardo Vinicius Kuhn,et al.  On the Steady-State Analysis of PNLMS-Type Algorithms for Correlated Gaussian Input Data , 2014, IEEE Signal Processing Letters.

[13]  Neil J. Bershad,et al.  Fast coupled adaptation for sparse impulse responses using a partial haar transform , 2005, IEEE Transactions on Signal Processing.

[14]  Ian Lewis,et al.  Nonlinear Acoustic Echo Cancellation Using Voltage and Current Feedback , 2015, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[15]  Lu Liu,et al.  l 0-norm penalised shrinkage linear and widely linear LMS algorithms for sparse system identification , 2017, IET Signal Process..

[16]  Haoxiang Wen,et al.  Parallel structure for sparse impulse response using moving window integration , 2017, IET Signal Process..