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Noise Reduction by Maximum a Posteriori Spectral Amplitude Estimation with Supergaussian Speech Modeling

Authors:
Lotter, T.Vary, P.
Book Title:
Proceedings of International Workshop on Acoustic Echo and Noise Control (IWAENC)
Address:
Kyoto, Japan
Date:
Sep. 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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