Frequency domain acoustic echo reduction based on Kalman smoother with time-varying noise covariance matrix

In this paper, we propose a novel acoustic-echo-reduction technique at a time-frequency domain, which is optimally combined with speech enhancement. Unlike conventional echo reduction techniques which minimizes only residual power of the far-end acoustic echo signal, the proposed method minimizes summation of the residual echo signal and distortion of the near-end speech signal from a minimum mean square error (MMSE) perspective. The proposed method performs echo reduction with speech enhancement and parameter optimization in an iterative manner based on the expectation-maximization (EM) algorithm. The E step is corresponding with the echo reduction and speech enhancement based on the Kalman smoother with a time-varying covariance matrix for the observation noise term, which reflects the time-varying characteristics of speech sources. By using the time-varying covariance matrix, we can enhance speech sources effectively with acoustic echo reduction. Associated with the time-varying covariance matrix, a new optimization scheme of parameters for the M step is derived in this paper. Experimental results with impulse responses which was recorded under a real meeting room show that the proposed method can effectively enhance a near-end speech signal when there are a near-end speech signal and a far-end acoustic echo signal.