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Minimum mean-square error amplitude estimators for speech enhancement under the generalized gamma distribution
- Author(s)
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- Richard Hendriks, (Delft Univ. of Technology)
- Jan Erkelens, (Delft Univ. of Technology)
- Jesper Jensen, (Delft Univ. of Technology)
- Richard Heusdens, (Delft Univ. of Technology)
- Topics
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- Noise reduction techniques
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Abstract
In this paper we derive minimum mean-square error (MMSE)amplitude estimators for DFT-based noise suppression. The optimal estimators are found under a generalized Gamma distribution, which takes as special cases (different parameter settings) all priors used in noise suppression schemes so far. Deriving the MMSE estimators involves integration of (weighted) Bessel functions. In order to end
up with analytical solutions, for some parameter settings we have to approximate the Bessel functions. In this paper we combine two typesof approximations by using a simple binary decision between the two. We show by computer simulations that the estimators thus obtained are very close to the exact MMSE estimators for all SNR conditions.
The presented estimators lead to improved performance compared to the suppression rule proposed by Ephraim and Malah. Furthermore, the maximum performance is the same as compared to state of the art amplitude estimators.
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