A priori SAP estimator based on the magnitude square coherence for dual-channel microphone system

In this paper, we present a time-frequency (TF)-dependent a priori speech absence probability (SAP) estimator utilizing the magnitude square coherence (MSC) between two microphone signals. It is shown that the normalized SNR can be numerically computed from the MSC by solving a quadratic equation. Based on the fact that the normalized SNR is bounded between 0 and 1, we directly use it for the probability of speech absence in each TF-unit. Since this approach does not require prior statistical knowledge of noise and speech, it is not affected by the performance of the noise PSD estimator. Furthermore, unlike the conventional SNR-based estimator, additional mapping strategy is unnecessary. The algorithm was evaluated using the receiver operating characteristic (ROC) curve and it attained higher correct detection rate at a given false-alarm rate than the conventional algorithms.