scholarly journals Folded Architecture for Non Canonical Least Mean Square Adaptive Digital Filter Used in Echo Cancellation

2016 ◽  
Vol 7 (3) ◽  
pp. 13-27 ◽  
Author(s):  
Pradnya Zode ◽  
Deshmukh A.Y
Keyword(s):  
Digital Filter ◽  
Echo Cancellation ◽  
Mean Square ◽  
Least Mean Square

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.

scholarly journals Study of Echo Cancellation approach by using Least Mean Square (LMS) Algorithm

2021 ◽  
Vol 1714 ◽  
pp. 012053
Author(s):  
I. PavanKalyan ◽  
G. Jaya Santosh ◽  
K.H.K. Prasad ◽  
Durgesh Nandan
Keyword(s):  
Echo Cancellation ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square

The Dynamic LMS for Line Echo Cancellation

2013 ◽  
Vol 284-287 ◽  
pp. 2941-2945
Author(s):  
Ning Yun Ku ◽  
Shaw Hwa Hwang ◽  
Shun Chieh Chang ◽  
Cheng Yu Yeh
Keyword(s):  
Impulse Response ◽  
Return Loss ◽  
Experimental Results ◽  
Echo Cancellation ◽  
Mean Square ◽  
Least Mean Square ◽  
Excellent Performance ◽  
Pure Delay ◽  
Echo Return Loss Enhancement ◽  
Computation Load

To the best of our knowledge, this study represents the proposal using the dynamic least mean square (DLMS) algorithm to reduce the computation load of LMS. Moreover, three regions of impulse response of line echo path are also proposed to analyze the redundant coefficients. Using the DLMS method, redundant coefficients can be detected and grouped, thereby automatically reducing computation. We employed line echo cancellation (LEC) to evaluate the performance of DLMS. The pure-delay and overlong regions of impulse response of line echo path are grouped and the associated computation load is reduced. The experimental results confirm the excellent performance of DLMS achieving a 35% savings in computation. Moreover, the quality echo return loss enhancement (ERLE) of DLMS also maintains at a level nearly equal to LMS.

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