Adaptive Multiple Sub-Filters Based Stereophonic Acoustic Echo Cancellation

2012 ◽  
Vol 433-440 ◽  
pp. 3022-3027
Author(s):  
Alaka Barik ◽  
Ravinder Nath ◽  
Asutosh Kar ◽  
Madhuparna Chakraborty
Keyword(s):  
Echo Cancellation ◽  
Parallel Structure ◽  
Acoustic Echo Cancellation ◽  
Least Mean Square ◽  
Echo Canceller ◽  
Adaptation Algorithm ◽  
Common Error ◽  
Acoustic Echo ◽  
Convergence Performance ◽  
Simulation Results

In this paper we present the Multiple Sub-Filters (MSF) parallel structure, Least Mean Square (LMS) adaptive algorithm, for Stereophonic Acoustic Echo Cancellation (SAEC). The convergence performance of the MSF parallel structure has been studied for two types of algorithms namely; different error algorithm and common error algorithm and has been compared with conventional echo canceller via computer simulations. Simulation results show that MSF with both adaptation algorithm provide better convergence speed as compared to the conventional SAEC realized by a Single Long Filter (SLF).

A New Echo Cancellation Algorithm without Double-Talk Detection Based on Correlation Function

2011 ◽  
Vol 225-226 ◽  
pp. 996-999
Author(s):  
Li Jun Sun ◽  
Shou Yong Zhang ◽  
Wei Sheng Wang ◽  
Xiao Ning Zhang
Keyword(s):  
Computer Simulation ◽  
Correlation Function ◽  
Adaptive Filter ◽  
Detection Algorithm ◽  
Echo Cancellation ◽  
Acoustic Echo Cancellation ◽  
Error Signal ◽  
Echo Canceller ◽  
Filter Performance ◽  
Simulation Results

In an adaptive echo canceller, the detection algorithm able to distinguish echo path change (EPC) from double-talk (DT) is vital to ensure that adaptive filter tap coefficients are updated in case of EPC and frozen during the DT period. The paper presents a new echo cancel algorithm, which can protect the adaptive filter performance during double-talk in acoustic echo cancellation of teleconference without setting a detector. A judgment value can be directly used in the iteration formula to control the iteration speed of the filter, which composed of the correlation of the far-end signal and near-end received signal, the pre-correlation of the error signal. The computer simulation results verify that the mentioned algorithm has the good double talk protection performance, and it is very useful and efficient in distinguishing EPC from DT but with less computational complexity contrast to the congener algorithm.

A New Switching QR Decomposition Adaptive Filtering Algorithm for Acoustic Echo Cancellation

2013 ◽  
Vol 303-306 ◽  
pp. 1077-1080
Author(s):  
Yi Zhou ◽  
Q. Li ◽  
T. Q. Zhang
Keyword(s):  
Adaptive Filtering ◽  
Echo Cancellation ◽  
Acoustic Echo Cancellation ◽  
Switching Scheme ◽  
State Estimate ◽  
Filtering Algorithm ◽  
Estimate Error ◽  
Fast Convergence Rate ◽  
Acoustic Echo ◽  
Convergence Performance

This paper proposes a new adaptive filtering algorithm based on the p-TA-QR-LS algorithm [1]. With a coefficient-derivative-based switching scheme, the new algorithm can work between two modes (p=1 and N) and achieve overall optimum convergence performance. The resultant switching p-TA-QR-LS algorithm is thus particularly suitable for acoustic echo cancellation (AEC) where both fast convergence rate and low steady-state estimate error are desired. Experiments are conducted to verify its improved overall convergence performance.

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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