NDLI: Speech enhancement based on minimum mean-square error estimation and supergaussian priors

Content Provider	IEEE Xplore Digital Library
Author	Martin, R.
Copyright Year	1993
Abstract	This paper presents a class of minimum mean-square error (MMSE) estimators for enhancing short-time spectral coefficients of a noisy speech signal. In contrast to most of the presently used methods, we do not assume that the spectral coefficients of the noise or of the clean speech signal obey a (complex) Gaussian probability density. We derive analytical solutions to the problem of estimating discrete Fourier transform (DFT) coefficients in the MMSE sense when the prior probability density function of the clean speech DFT coefficients can be modeled by a complex Laplace or by a complex bilateral Gamma density. The probability density function of the noise DFT coefficients may be modeled either by a complex Gaussian or by a complex Laplacian density. Compared to algorithms based on the Gaussian assumption, such as the Wiener filter or the Ephraim and Malah (1984) MMSE short-time spectral amplitude estimator, the estimators based on these supergaussian densities deliver an improved signal-to-noise ratio.
Starting Page	845
Ending Page	856
Page Count	12
File Size	604768
File Format	PDF
ISSN	10636676
Volume Number	13
Issue Number	5
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-09-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speech enhancement Estimation error Discrete Fourier transforms Gaussian noise Speech analysis Probability density function Amplitude estimation Laplace equations Wiener filter Signal to noise ratio supergaussian (leptokurtic) densities Minimum mean-square error (MMSE) spectral estimation minimum statistics noise power estimation noise reduction
Content Type	Text
Resource Type	Article
Subject	Acoustics and Ultrasonics Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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