  Traditionally, adaptive filters have been deployed to achieve AEC by
estimating the acoustic echo response using algorithms such as the Normalized
Least-Mean-Square (NLMS) algorithm. Several approaches have been proposed over
recent years to improve the performance of the standard NLMS algorithm in
various ways for AEC. These include algorithms based on Time Domain, Frequency
Domain, Fourier Transform, Wavelet Transform Adaptive Schemes, Proportionate
Schemes, Proportionate Adaptive Filters, Combination Schemes, Block Based
Combination, Sub band Adaptive Filtering, Uniform Over Sampled DFT Filter
Banks, Sub band Over-Sampled DFT Filter Banks, Volterra Filters, Variable
Step-Size (VSS) algorithms, Data Reusing Techniques, Partial Update Adaptive
Filtering Techniques and Sub band (SAF) Schemes. These approaches aim to
address issues in echo cancellation including the performance with noisy input
signals, Time-Varying echo paths and computational complexity. In contrast to
these approaches, Sparse Adaptive algorithms have been developed specifically
to address the performance of adaptive filters in sparse system identification.
In this paper we have discussed some AEC algorithms followed by comparative
study with respective to step-size, convergence and performance.
