Speech Enhancement by MAP Spectral Amplitude Estimation Using a Super-Gaussian Speech Model

被引：0

作者：

Thomas Lotter

Peter Vary

机构：

[1] RWTH Aachen University of Technology,Institute of Communication Systems and Data Processing

[2] RWTH Aachen,undefined

[3] Siemens Audiological Engineering Group,undefined

来源：

EURASIP Journal on Advances in Signal Processing | / 2005卷

关键词：

speech enhancement; MAP estimation; speech model;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

This contribution presents two spectral amplitude estimators for acoustical background noise suppression based on maximum a posteriori estimation and super-Gaussian statistical modelling of the speech DFT amplitudes. The probability density function of the speech spectral amplitude is modelled with a simple parametric function, which allows a high approximation accuracy for Laplace- or Gamma-distributed real and imaginary parts of the speech DFT coefficients. Also, the statistical model can be adapted to optimally fit the distribution of the speech spectral amplitudes for a specific noise reduction system. Based on the super-Gaussian statistical model, computationally efficient maximum a posteriori speech estimators are derived, which outperform the commonly applied Ephraim-Malah algorithm.

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