Minima-controlled speech presence uncertainty tracking method for speech enhancement

Abstract

In speech enhancement, soft decision, in which the speech absence probability (SAP) is introduced to modify the spectral gain or update the noise power, is known to be efficient. In many previous works, a fixed a priori probability of speech absence ( q) is assumed in estimating the SAP, which is not realistic since speech is quasi-stationary and may not be present in each frequency bin. To address this problem, Malah et al. devised a novel method to obtain distinct values of q for each frequency bin in many frames by comparing the a posteriori SNR to a threshold value [9]. In this regard, a novel algorithm is achieved by taking an advantage of a minima-controlled recursive averaging (MCRA) technique that allows for the robust tracking of speech absence in time. This leads to the improved tracking performance of speech absence in speech enhancement and better results in the objective and subjective evaluation tests.