Adaptive filtering is at the core of many
signal processing applications as phenomenal advances
both in research and application have been made
during the past three decades. The main objective of an
adaptive filter is to minimize error signal at the cost of
high convergence rate and reduced computational
complexity. This paper presents the performance of
adaptive filter algorithms for acoustic echo cancellation
by eliminating the echo signal from the original signal.
The adaptive filtering process is carried out by using
Least Mean Square (LMS), Normalized-Least Mean
Square (NLMS) and Kalman filter in a real-time non-
stationary environment and its performance is
measured in terms of convergence rate, Mean Square
Error and Echo Return Loss Enhancement (ERLE).
Simulations are carried out by using MatLab and the
output results show clearly that Kalman filter converges
after about 7000 iterations and outperforms LMS and
NLMS algorithms with 42dB ERLE against 12dB
ERLE and 20dB ERLE for LMS and NLMS
respectively. Therefore, Kalman filter is more suitable
for echo cancellation in a non-stationary environment.
Keywords : Acoustic Echo Cancellation, Adaptive filtering, LMS, NLMS, Kalman filter.