Lessons in digital estimation theory
Lessons in digital estimation theory
Discrete-time signal processing
Discrete-time signal processing
Time series: data analysis and theory
Time series: data analysis and theory
Speech enhancement based on a priori signal to noise estimation
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 02
Low complexity DFT-domain noise PSD tracking using high-resolution periodograms
EURASIP Journal on Advances in Signal Processing
Impact of SNR and gain-function over- and under-estimation on speech intelligibility
Speech Communication
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The Minimum Statistics noise power spectral density (psd) estimation approach is based on tracking minima of a short term power spectral density (psd) estimate in frequency subbands. Since the short term minimum power is always smaller than (or in trivial cases equal to) the mean power, the minimum noise power estimate is a biased estimate of the mean power. For an accurate noise power estimate this bias must be compensated.In this paper we review bias compensation methods for moving average and first-order recursive smoothed psd estimates. While for some cases exact expressions for the bias are available, approximations are required in general. We present approximations which allow an efficient computation and compensation of the bias. We discuss factors that influence the bias and show that the method is to some extent robust to variations of the signal statistics. Besides different smoothing methods, we discuss the effect of overlapping spectral analysis windows and of signal correlation.