Speech enhancement based on Rayleigh mixture modeling of speech spectral amplitude distributions

DFT-based speech enhancement algorithms typically rely on a statistical model of the spectral amplitudes of the noise-free speech signal. It has been shown in the literature recently that the speech spectral amplitude distributions, conditional on estimated a priori SNR, may differ significantly from the traditional Gaussian model and are better described by super-Gaussian probability density functions. We show that these conditional distributions can be accurately approximated by a mixture of Rayleigh distributions. The MMSE amplitude estimators based on Rayleigh Mixture Models perform at least as well as the estimators based on super-Gaussian models. Furthermore, the proposed Rayleigh Mixture Models allow for derivation of closed-form estimators minimizing other perceptually relevant distortion measures, which may be difficult for other models.