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

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.

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Correspondence to Thomas Lotter.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Lotter, T., Vary, P. Speech Enhancement by MAP Spectral Amplitude Estimation Using a Super-Gaussian Speech Model. EURASIP J. Adv. Signal Process. 2005, 354850 (2005). https://doi.org/10.1155/ASP.2005.1110

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Keywords and phrases

  • speech enhancement
  • MAP estimation
  • speech model