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 multi-dimensional 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 ≤ γ ≤ 1, with γ = 0 corresponding to the lower bound and γ = 1 to the upper bound. Experimental results show that the proposed estimators achieve a better performance than existing estimators, especially at high SNR.