A CONVEX OPTIMIZATION APPROACH FOR TIME-FREQUENCY MASK ESTIMATION

In this paper, we propose a new time-frequency mask method for computational auditory scene analysis (CASA) based on convex optimization of the binary mask. In the proposed method, the pitch estimation and segment segregation in conventional CASA are completely replaced by the convex optimization of speech power. Considering the cross-correlation between the power spectra of noisy speech and noise in each of a Gammatone filterbank channel, the objective function of speech power used for convex optimization is built. The speech power is estimated by gradient descent method. Thus, the time-frequency units dominated by speech and noise are labeled by comparing the powers of noisy and estimated speech, and noise. The erroneous local masks are also removed by using the Teager energy of the estimated speech and time-frequency unit smoothing. The results from the average segmental signal-to-noise ratio improvement, HIT-False Alarm rate and subjective test show that the performance of the proposed method outperforms the reference methods.