Abstract:
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 literat...Show MoreMetadata
Abstract:
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.
Published in: 2007 15th European Signal Processing Conference
Date of Conference: 03-07 September 2007
Date Added to IEEE Xplore: 04 May 2015
Print ISBN:978-839-2134-04-6
Conference Location: Poznan, Poland