Publications – Details
Speech Enhancement by MAP Spectral Amplitude Estimation using a Super-Gaussian Speech Model
- Authors:
- Thomas Lotter and Peter Vary
- Journal:
- EURASIP Journal on Applied Signal Processing
- Volume:
- 2005
- Number:
- 7
- Date:
- May 2005
- Pages:
- 1110–1126
- Language:
- English
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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