Improved noise suppression filter using self-adaptive estimator of probability of speech absence

In this paper, two estimators of the probability of speech absence are derived using the common assumption that the Fourier coefficients of a frame of speech and noise samples are statistically independent Gaussian random variables (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 spectral amplitude. Each of the time-adaptive estimators produces an estimate of the probability of speech absence for each spectral frequency. The estimated probability will be higher during the speech period and lower during the silence period. The estimated probability can be fed directly to any filter which requires such an estimate, e.g. the Ephraim and Malah noise suppressor (Ephraim and Malah, 1984), and the modified power subtraction method (Scalart and Vieira Filho, 1996), with significant improvements for various noise types. (C) 1999 Elsevier Science B.V. All rights reserved.