Publications – Details
Noise Reduction by Maximum a Posteriori Spectral Amplitude Estimation with Supergaussian Speech Modeling
- Authors:
- Thomas Lotter and Peter Vary
- Book Title:
- Proceedings of International Workshop on Acoustic Echo and Noise Control (IWAENC)
- Venue:
- Kyoto, Japan
- Date:
- Sept. 2003
- Language:
- English
Abstract
This contribution presents a spectral amplitude estimator for acoustical background noise suppression based on maximum a posteriori estimation and supergaussian statistical modeling of the speech DFT coefficients. The probability density function of the speech spectral amplitude is modeled 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. Based on the approximation, a computationally efficient maximum a posteriori speech estimator is derived, which outperforms the Ephraim-Malah algorithm in a single channel noise reduction framework.
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