Solving the Acoustic Echo Cancellation Problem in Double-Talk Scenario Using Non-Gaussianity of the Near-End Signal

The acoustic echo cancellation (AEC) problem in the double-talk scenario and the blind source separation (BSS) problem resemble each other in that, for both problems, mixed signals are given and the objectives are to remove unwanted signals from the mixed signals. As many BSS algorithms have utilized the non-Gaussianity of the source signals to solve the separation problem, the super-Gaussianity of the near-end speech signal can be utilized to perform AEC in the double-talk scenario. Here, we propose a maximum likelihood (ML) approach using a super-Gaussian source prior to solve the double-talk-scenario AEC problem and compare the algorithm with minimizing mean squared error (MSE). The simulation results and analysis support the efficiency of the proposed method.