Abstract
Speech signals are frequently disturbed by statistically independent additive noise signals. When the power fluctuation of the noise signal is significantly 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. We present some of the more prominent estimation methods for complex spectral coefficients, for the amplitude and phase of spectral coefficients, and for related parameters such as the a priori signal-to-noise ratio. We interpret these algorithms in terms of their input-output characteristics. Some recent 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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Martin, R. (2005). Statistical Methods for the Enhancement of Noisy Speech. In: Speech Enhancement. Signals and Communication Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-27489-8_3
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DOI: https://doi.org/10.1007/3-540-27489-8_3
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