On linear versus non-linear magnitude-DFT estimators and the influence of super-Gaussian speech priors

Although the linear mean-squared error (MSE) complex-DFT estimator, i.e., the Wiener filter, is well-known, its magnitude-DFT (MDFT) counterpart has never been considered in the context of speech enhancement. Therefore, certain theoretical questions regarding MDFT estimators remained unanswered. For example, it is unknown to which extend the performance of existing MSE MDFT estimators depends on the chosen speech prior, or on the non-linearity of the estimators. In this paper we present linear MSE MDFT estimators for speech enhancement. In contrast to the linear complex-DFT estimator, the presented linear MSE MDFT estimators do depend on the assumed distribution of the speech DFT coefficients. Based on objective and subjective experiments, it can be concluded that the chosen speech prior, i.e., Gaussian versus super-Gaussian has a significant effect on the performance of MDFT estimators, while the linearity as compared to non-linearity has only a minor influence.