A Statistical Model-Based Residual Echo Suppression

In this letter, we propose a novel residual echo suppression (RES) algorithm based on a statistical model constructed in the acoustic echo cancellation framework. In the proposed approach, all the possible near-end and far-end signal conditions are classified into four distinct hypotheses, and the power spectral density estimation is carried out according to the result of hypothesis testing. The distribution of each signal component is characterized by a parametric model, and the conventional likelihood ratio test is performed to make an optimal decision. The experimental results show that the proposed algorithm yields improved performance compared to that of the previous RES technique.