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Traditionally, adaptive filters have been deployed to achieve AEC byestimating the acoustic echo response using algorithms such as the NormalizedLeast-Mean-Square (NLMS) algorithm. Several approaches have been proposed overrecent years to improve the performance of the standard NLMS algorithm invarious ways for AEC. These include algorithms based on Time Domain, FrequencyDomain, Fourier Transform, Wavelet Transform Adaptive Schemes, ProportionateSchemes, Proportionate Adaptive Filters, Combination Schemes, Block BasedCombination, Sub band Adaptive Filtering, Uniform Over Sampled DFT FilterBanks, Sub band Over-Sampled DFT Filter Banks, Volterra Filters, VariableStep-Size (VSS) algorithms, Data Reusing Techniques, Partial Update AdaptiveFiltering Techniques and Sub band (SAF) Schemes. These approaches aim toaddress issues in echo cancellation including the performance with noisy inputsignals, Time-Varying echo paths and computational complexity. In contrast tothese approaches, Sparse Adaptive algorithms have been developed specificallyto address the performance of adaptive filters in sparse system identification.In this paper we have discussed some AEC algorithms followed by comparativestudy with respective to step-size, convergence and performance.

Comparative Study of Acoustic Echo Cancellation Algorithms for Speech Recognition System in Noisy Environment