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  • Research Article
  • Open Access

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

EURASIP Journal on Advances in Signal Processing20052005:354850

  • Received: 7 June 2004
  • Published:


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 enhancement
  • MAP estimation
  • speech model

Authors’ Affiliations

Institute of Communication Systems and Data Processing, RWTH Aachen University of Technology, RWTH Aachen, Aachen, 52056, Germany
Siemens Audiological Engineering Group, Gebbertstrasse 125, Erlangen, 91058, Germany