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Title: Single Channel Speech Enhancement: MAP Estimation Using GGD Prior Under Blind Setup
Authors: Raj Kishore Prasad
Hiroshi Saruwatari
Kiyohiro Shikano
Issue Date: Sep-2004
Publisher: Springer
Start page: 873
End page: 880
Abstract: This paper presents a statistical algorithm using Maximum A Posteriori (MAP) estimation for the enhancement of single channel speech, contaminated by the additive noise, under the blind framework. The algorithm uses Generalized Gaussian Distribution (GGD) function as a prior probability to model magnitude of the Spectral Components (SC) of the speech and noise in the frequency domain. An estimation rule has been derived for the estimation of the SC of the clean speech signal under the presence of additive noise signal. Since the parsimony of the GGD distribution depends on its shape parameter, it provides flexible statistical model for the data with different distribution, e.g. impulsive, Laplacian, Gaussian, etc. The enhancement result for Laplacian noise have been presented and compared with that of the conventional Wiener filtering, which assumes Gaussian distribution for SCs of both the speech and noise.
Description: ICA2004: the 5th International Symposium on Independent Component Analysis and Blind Signal Separation, September 22-24, 2004, Granada, Spain.
ISBN: 9783540301103
Rights: Copyright 2004 Springer-Verlag Berlin Heidelberg
Text Version: Publisher
Publisher DOI: 10.1007/978-3-540-30110-3_110
Appears in Collections:情報科学研究科 / Graduate School of Information Science

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