An EM-based Probabilistic Approach for Acoustic Echo Suppression

This paper introduces a new Acoustic Echo Suppression (AES) algorithm for suppressing the residual echo after the Acoustic Echo Canceller (AEC). By temporally segmenting the frequency bins of the residual signal spectrum into blocks and modelling the data in each block and each frequency bin as realizations of a random variable, we can compute the probability of presence of residual echo and derive an appropriate ML suppression rule based on this probability. The computation of the probabilities is based on the Expectation Maximization algorithm. The proposed method shows better performance as compared to state of the art methods for residual echo suppression while producing no audible degradation in the near end signal and no musical noise. Test results indicate that the proposed approach provides an increase in the ERLE of up to 3 dB more than the state of the art echo suppressor while yielding a comparable mean opinion score (MOS) for the near end speech quality. Furthermore, the proposed method is independent of the double talk detector – which makes it robust to misclassifications on the part of the AEC algorithm.