Iy. Soon et al., Improved noise suppression filter using self-adaptive estimator of probability of speech absence, SIGNAL PROC, 75(2), 1999, pp. 151-159
In this paper, two estimators of the probability of speech absence are deri
ved using the common assumption that the Fourier coefficients of a frame of
speech and noise samples are statistically independent Gaussian random var
iables (Ephraim and Malah, 1984; McAulay and Malpass, 1980). The estimators
are obtained directly from the noisy speech itself. The first estimator is
obtained by binary classification of the received spectral amplitude into
speech present or speech absent state. The second estimator is obtained by
deriving the conditional probability of speech absence given the received s
pectral amplitude. Each of the time-adaptive estimators produces an estimat
e of the probability of speech absence for each spectral frequency. The est
imated probability will be higher during the speech period and lower during
the silence period. The estimated probability can be fed directly to any f
ilter which requires such an estimate, e.g. the Ephraim and Malah noise sup
pressor (Ephraim and Malah, 1984), and the modified power subtraction metho
d (Scalart and Vieira Filho, 1996), with significant improvements for vario
us noise types. (C) 1999 Elsevier Science B.V. All rights reserved.