Speech dereverberation in noisy environments using an adaptive minimum mean square error estimator

Abstract

The authors present here a novel method for reducing the late reverberation of speech signals in noisy environments. In this method, the amplitude of clean signal is obtained by an adaptive estimator that minimises the mean square error (MSE) under signal presence uncertainty. The spectral gain function, that is an adaptive variable-order minimum MSE estimator, is obtained as a weighted geometric mean of hypothetical gains associated with speech presence and absence. The order of estimator is estimated for each time frame and each frequency component individually. The authors propose the adaptation of order of estimator according to the probability of speech presence, which makes the estimation more accurate. The evaluations confirm superiority of the proposed method in dereverberation of speech signals in noisy environments.