An adaptive β-order MMSE estimator for speech enhancement using super-Gaussian speech model - 文章详情页

作者：

An, Shan (An, Shan.) | Bao, Chang-Chun (Bao, Chang-Chun.) (学者：鲍长春) | Xia, Bing-Yin (Xia, Bing-Yin.)

收录：

EI Scopus

摘要：

This　paper　proposes　an　adaptive　β-order　Minimum-Mean-Square-Error　(MMSE)　estimator　for　speech　enhancement　using　super-Gaussian　speech　model　(β-SG-MMSE).　The　spectral　amplitude　of　clean　speech　is　estimated　by　MMSE　estimator　under　the　assumption　that　the　DFT　coefficients　of　clean　speech　are　modeled　by　super-Gaussian　distribution　and　the　DFT　coefficients　of　noise　signal　are　modeled　by　Gaussian　distribution.　Then,　the　speech　presence　probability　under　super-Gaussian　model　is　introduced　into　the　proposed　estimator.　In　order　to　obtain　a　good　trade-off　between　noise　suppression　and　speech　distortion,　the　order　β　of　estimator　is　updated　adaptively　according　to　the　Signal-to-Noise　Ratio　(SNR)　in　each　sub-band.　The　result　of　performance　evaluation　by　ITU-T　G.160　shows　that　the　overall　performance　of　the　proposed　method　is　better　than　the　reference　algorithms　in　white　noise　and　color　noise.　©　2013　IEEE.

关键词：

Signal to noise ratio White noise Economic and social effects Gaussian noise (electronic) Gaussian distribution Speech enhancement Mean square error

作者机构：

[ 1 ] [An, Shan]Speech and Audio Signal Processing Laboratory, School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China
[ 2 ] [Bao, Chang-Chun]Speech and Audio Signal Processing Laboratory, School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China
[ 3 ] [Xia, Bing-Yin]Speech and Audio Signal Processing Laboratory, School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China

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