A Statistical Model-Based Speech Enhancement Using Acoustic Noise Classification for Robust Speech Communication
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概要
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In this paper, we present a speech enhancement technique based on the ambient noise classification that incorporates the Gaussian mixture model (GMM). The principal parameters of the statistical model-based speech enhancement algorithm such as the weighting parameter in the decision-directed (DD) method and the long-term smoothing parameter of the noise estimation, are set according to the classified context to ensure best performance under each noise. For real-time context awareness, the noise classification is performed on a frame-by-frame basis using the GMM with the soft decision framework. The speech absence probability (SAP) is used in detecting the speech absence periods and updating the likelihood of the GMM.
- 2012-07-01
著者
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Chang Joon-hyuk
School Of Electronic And Electrical Engineering Inha University
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Choi Jae-hun
School Of Electronic Engineering Hanyang University
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CHOI Jae-Hun
School of Electronic Engineering, Hanyang University
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