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We consider estimation of the noise spectral variance from speech signals contaminated by highly nonstationary noise sources. In each time frame, for each frequency bin, the noise variance estimate is updated recursively with the Minimum Mean-Square Error (MMSE) estimate of the current noise power. For the estimation of the noise power, a spectral gain(More)
—This paper considers estimation of the noise spectral variance from speech signals contaminated by highly nonsta-tionary noise sources. The method can accurately track fast changes in noise power level (up to about 10 dB/s). In each time frame, for each frequency bin, the noise variance estimate is updated recursively with the minimum mean-square error(More)
— A time-domain analysis of the autocorrelation method for autoregressive estimation is given. It is shown that a small bias in a reflection coefficient close to one in absolute value is propagated and prohibits an accurate estimation of further reflection coefficients. Tapered data windows largely reduce this effect, but increase the variance of the models.
—This paper considers suppression of late reverberation and additive noise in single-channel speech recordings. The reverberation introduces long-term correlation in the observed signal. In the first part of this work, we show how this correlation can be used to estimate the late reverberant spectral variance (LRSV) without having to assume a specific model(More)
—This letter considers the estimation of speech signals contaminated by additive noise in the discrete Fourier transform (DFT) domain. Existing complex-DFT estimators assume indepen-dency of the real and imaginary parts of the speech DFT coefficients , although this is not in line with measurements. In this letter, we derive some general results on these(More)