Sparse Adaptive Filters for Echo Cancellation

2010 ◽  
Vol 6 (1) ◽  
pp. 1-124 ◽  
Author(s):  
Constantin Paleologu ◽  
Jacob Benesty ◽  
Silviu Ciochina
Keyword(s):  
Adaptive Filters ◽  
Echo Cancellation

Nonlinear acoustic echo cancellation with kernelized adaptive filters

2020 ◽  
Vol 166 ◽  
pp. 107329
Author(s):  
Sanjana Sankar ◽  
Asutosh Kar ◽  
Srikanth Burra ◽  
M.N.S. Swamy ◽  
Vladimir Mladenovic
Keyword(s):  
Adaptive Filters ◽  
Echo Cancellation ◽  
Acoustic Echo Cancellation ◽  
Acoustic Echo ◽  
Nonlinear Acoustic

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.

scholarly journals 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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