DTD-free AEC via a Sliding DFT Window for ICA-based single Parameter Estimation

In this paper, an independent component analysis (ICA) acoustic echo cancellation (AEC) algorithm is introduced where a sliding discrete Fourier transform window is adopted such that there is only one AEC parameter to estimate (reduced computational load), as opposed to thousands of coefficients modeling the room response. Conventional adaptive filtering techniques such as the least mean square (LMS) algorithm often fail under double-talk condition (and excessive noise) due to a corrupted measure of the objective function (i.e. minimization of the error output). Recent study has shown that ICA allows continual adaptation of the AEC parameters, hence it is adopted here as the optimization method of our AEC parameter. Simulation results are used to illustrate the superiority of the proposed algorithm over the LMS methods.