A FAMILY OF BAYESIAN STSA ESTIMATORS FOR THE ENHANCEMENT OF SPEECH WITH CORRELATED FREQUENCY COMPONENTS

In Bayesian short-time spectral amplitude (STSA) estimation for speech enhancement, the spectral components are traditionally assumed uncorrelated. However, this assumption is inexact since some correlation is present in practice. We thus investigate a multidimensional STSA estimator that assumes correlated frequency components. Since the closed-form solution of this optimum estimator is not readily available, we previously derived closed-form expressions for an upper and a lower bound on the desired estimator. In this paper, we study the proximity between the upper and the lower bounds and propose a new family of estimators that are derived from these bounds and characterized by a scalar parameter 0 <= gamma <= 1, with gamma = 0 corresponding to the lower bound and gamma = 1 to the upper bound. Experimental results show that the proposed estimators achieve a better performance than existing estimators, especially at high SNR.