Titlebook: Speech Enhancement; Jacob Benesty,Shoji Makino,Jingdong Chen Book 2005 Springer-Verlag Berlin Heidelberg 2005 Interface.Signal.Universal M

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Statistical Methods for the Enhancement of Noisy Speech, developments such as the use of super-Gaussian speech models and the properties of the resulting estimators are highlighted. Furthermore, we discuss the estimation of the background noise power and the application of these techniques in conjunction with a low bit rate speech coder.

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From Volatility Modeling of Financial Time-Series to Stochastic Modeling and Enhancement of Speech me-series. Spectral analysis shows that speech signals in the STFT domain are also characterized by heavy tailed distributions and volatility clustering. We demonstrate the application of GARCH modeling to speech enhancement, and show its advantage compared to using the conventional decision-directed method.

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Real-Time Blind Source Separation for Moving Speech Signals,performance than an online algorithm when sources are stationary, and the postprocessing compensates for performance degradation caused by source movement. Experimental results using speech signals recorded in a real room show that our method realizes robust real-time separation for moving sources.

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1860-4862 lecommunications, hands-free communications, recording, human-machine interfaces, etc.) that require at least one microphone, the signal of interest is usually contaminated by noise and reverberation. As a result, the microphone signal has to be "cleaned" with digital signal processing tools before

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Statistical Methods for the Enhancement of Noisy Speech,cantly slower than that of the speech signal, a single-microphone approach may be successfully used to reduce the level of the disturbing noise. This chapter outlines algorithms for noise reduction which are based on short term spectral representations of speech and on optimal estimation techniques.

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Single- and Multi-Microphone Spectral Amplitude Estimation Using a Super-Gaussian Speech Model,tors with a super-Gaussian speech model, that can be adapted with high accuracy towards the real distribution in a given system, are introduced. For multi-microphone applications, joint MAP estimators that also exploit spatial properties of speech and noise are derived. Both the integration of the m