EURASIP Journal on Applied Signal Processing 
Volume 2005 (2005), Issue 7, Pages 1110-1126
doi:10.1155/ASP.2005.1110

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

Thomas Lotter1,2 and Peter Vary1

 1Institute of Communication Systems and Data Processing, RWTH Aachen University of Technology, RWTH Aachen, Aachen 52056, Germany
2Siemens Audiological Engineering Group, Gebbertstrasse 125, Erlangen 91058, Germany
Received 7 June 2004; Revised 17 September 2004

Recommended by Jacob Benesty

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.