Acoustic echo cancellation using a computationally efficient transform domain LMS adaptive filter

2010 ◽  
Author(s):  
E. Hari Krishna ◽  
M. Raghuram ◽  
K. Venu Madhav ◽  
K. Ashoka Reddy
Keyword(s):  
Adaptive Filter ◽  
Echo Cancellation ◽  
Acoustic Echo Cancellation ◽  
Transform Domain ◽  
Computationally Efficient ◽  
Acoustic Echo

scholarly journals Simple and Efficient Double-Talk-Detector for Acoustic Echo Cancellation

2020 ◽  
Vol 37 (4) ◽  
pp. 585-592
Author(s):  
Mourad Benziane ◽  
Mohamed Bouamar ◽  
Mouldi Makdir
Keyword(s):  
Convergence Rate ◽  
Adaptive Filter ◽  
Early Stage ◽  
Recursive Estimation ◽  
Echo Cancellation ◽  
Recursive Least Squares ◽  
Acoustic Echo Cancellation ◽  
Decision Variable ◽  
Rls Algorithm ◽  
Acoustic Echo

Acoustic Echo Cancellation (AEC) is a topic that has received a great interest in recent years. However, a significant challenge remains with the problem of double-talk especially when the adaptive filter has a fast convergence rate. In this case, the double-talk detector (DTD) must reply in early stage and halt updating of the adaptive filter in order to avoid filter coefficients divergence. Indeed, a complex and inappropriate DTD can seriously affect the convergence rate of the adaptive filter and global performances of the AEC system. In this paper, an implementation of a simple and efficient DTD based on a recursive estimation of the decision variable which is resulting from the level comparison between far-end and microphone signals is proposed. The presented algorithm is then compared with the normalized cross-correlation (NCC) method which is taken as a reference in this work. In the simulation tests, the recursive least squares (RLS) algorithm is used to update the adaptive filter coefficients. The speech signals used in the tests are taken from the TIMIT database.

Acoustic Echo Cancellation using Computationally Efficient Adaptive Algorithm Techniques

2016 ◽  
Vol 1 (2) ◽  
pp. 57-62
Author(s):  
Mastan Sharif Shaik ◽  
K. Satya Prasad ◽  
Rafi Ahamed Shaik ◽  
D. Venkata Rao
Keyword(s):  
Adaptive Filters ◽  
Mean Squared Error ◽  
Echo Cancellation ◽  
Lms Algorithm ◽  
Acoustic Echo Cancellation ◽  
Computationally Efficient ◽  
Primary Input ◽  
Squared Error ◽  
Acoustic Echo ◽  
The Mean

Several sign based LMS adaptive filters, which are computationally free having multiplier free weight update loops, are proposed for acoustic echo cancellation. The adaptive filters essentially minimizes the mean- squared error between a primary input, which is the echo, and a reference input, which is either echo that is correlated in some way with the echo in the primary input. The results show that the performance of the signed regressor. LMS algorithm is superior than conventional LMS algorithm, the performance of signed LMS and sign- sign LMS based realizations are comparable to that of the LMS based filtering techniques in terms of Average Attenuation and computational complexity.

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