Speech presence probability estimation based on integrated time-frequency minimum tracking for speech enhancement in adverse environments

Speech enhancement under nonstationary environments is a challenging problem. This paper addresses the problem of speech presence probability (SPP) estimation. According to the fact that speech is approximately sparse in time-frequency domain, we integrate time and frequency minimum tracking results to estimate the noise power spectral density and the a posteriori signal-to-noise ratio. A sparseness measure is proposed to adjust the SPP estimates. By applying Bayes rule, we present the final SPP estimates, which control the time varying smoothing of the noise power spectrum. We show that under slowly and highly nonstationary noise conditions, the integrated minimum tracking (IMT) approach can update the noise estimates faster than the competitive methods. When integrated into a speech enhancement system, it achieves improved speech quality and lower residual noise.