Minimum Mean-Square Error Amplitude estimator for speech enhancement under the generalized gamma distribution

In this paper we derive minimum mean-square error (MMSE) amplitude estimators for DFT-based noise suppression. The op-timal estimators are found under a generalized Gamma distribu-tion, which takes as special cases (different parameter settings) all priors used in noise suppression schemes so far. Deriving the MMSE estimators involves integration of (weighted) Bessel functions. In order to end up with analytical solutions, for some parameter settings we have to approximate the Bessel functions. In this paper we combine two types of approximations by using a simple binary decision between the two. We show by computer simulations that the estimators thus obtained are very close to the exact MMSE estimators for all SNR conditions. The presented estimators lead to improved performance compared to the sup-pression rule proposed by Ephraim and Malah. Furthermore, the maximum performance is the same as compared to state of the art amplitude estimators. 1