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Open Access

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

EURASIP Journal on Advances in Signal Processing20052005:354850

https://doi.org/10.1155/ASP.2005.1110

Received: 7 June 2004

Published: 22 May 2005

Abstract

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.

Keywords and phrases

speech enhancementMAP estimationspeech model

Authors’ Affiliations

(1)
Institute of Communication Systems and Data Processing, RWTH Aachen University of Technology, RWTH Aachen, Aachen, Germany
(2)
Siemens Audiological Engineering Group, Erlangen, Germany

Copyright

© T. Lotter and P. Vary 2005

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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