Error-dependent step-size control of adaptive normalized least-mean-square filters used for nonlinear acoustic echo cancellation

2015 ◽  
Vol 10 (3) ◽  
pp. 511-518 ◽  
Author(s):  
Cristian Contan ◽  
Botond Sandor Kirei ◽  
Marina Dana Topa
Keyword(s):  
Size Control ◽  
Echo Cancellation ◽  
Acoustic Echo Cancellation ◽  
Mean Square ◽  
Least Mean Square ◽  
Step Size ◽  
Step Size Control ◽  
Normalized Least Mean Square ◽  
Acoustic Echo ◽  
Nonlinear Acoustic

A Robust Step-Size Control Technique Based on Proportionate Constraints on Filter Update for Acoustic Echo Cancellation

2016 ◽  
Vol 25 (4) ◽  
pp. 692-699 ◽  
Author(s):  
Chao Wu ◽  
Yanmeng Guo ◽  
Yonghong Yan ◽  
Xiaofei Wang ◽  
Qiang Fu ◽  
...  
Keyword(s):  
Size Control ◽  
Echo Cancellation ◽  
Control Technique ◽  
Acoustic Echo Cancellation ◽  
Step Size ◽  
Step Size Control ◽  
Acoustic Echo

Step-size control for acoustic echo cancellation filters – an overview

2000 ◽  
Vol 80 (9) ◽  
pp. 1697-1719 ◽  
Author(s):  
Andreas Mader ◽  
Henning Puder ◽  
Gerhard Uwe Schmidt
Keyword(s):  
Size Control ◽  
Echo Cancellation ◽  
Acoustic Echo Cancellation ◽  
Step Size ◽  
Step Size Control ◽  
Acoustic Echo

A Joint Optimized Robust Acoustic Echo Cancellation Algorithm

2019 ◽  
Vol 33 (09) ◽  
pp. 1959029 ◽  
Author(s):  
Hongyan Li ◽  
Jianghao Feng ◽  
Yue Wang ◽  
Xueying Zhang
Keyword(s):  
Impulse Noise ◽  
Regularization Parameter ◽  
Convergence Speed ◽  
Echo Cancellation ◽  
Acoustic Echo Cancellation ◽  
Time Varying ◽  
Mean Square ◽  
Least Mean Square ◽  
Acoustic Echo ◽  
Series Of Experiments

When the input signals for acoustic echo cancellation (AEC) are related signals, the convergence speed of the traditional normalized least mean square (NLMS) algorithms is significantly reduced. In this paper, a joint optimization robust AEC algorithm is proposed to solve this problem. Based on the analysis of the convergence of the normalized subband adaptive filtering (NSAF) algorithm, the algorithm is optimized by minimizing the mean square error (MSE) of the NSAF algorithm, combining sub-band time-varying step factor and time-varying regularization parameter to update the filter weight vectors. And when the impulse noise occurs, the sub-band cut-off parameter is updated in a recursive manner, which makes the algorithm achieve fast convergence speed and low steady-state error, and has strong robustness to impulse noise. In a series of experiments on AEC, simulation results show that the performance of the algorithm is better than the existing algorithms.

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