Fast Nonstationary Noise Tracking Based on Log-Spectral Power MMSE Estimator and Temporal Recursive Averaging

Abstract

Estimation of the noise power spectral density (PSD) plays a critical role in most existing single-channel speech enhancement algorithms. In this paper, we present a novel noise PSD tracking algorithm, which employs a log-spectral power minimum mean square error (MMSE) estimator. This method updates the noise PSD estimate by performing a temporal recursive averaging of log-spectral MMSE estimate of the current noise power to reduce the risk of speech leakage into noise estimate. A smoothing parameter used in the recursive operation is adjusted by speech presence probability (SPP). In this method, a spectral nonlinear weighting function is derived to estimate the noise spectral power which depends on the a priori and the a posteriori signal-to-noise ratio (SNR). An extensive performance comparison has been carried out with several state-of-the-art noise tracking algorithms, i.e., Minimum Statistics (MS), modified minima controlled recursive averaging algorithm (MCRA-2), MMSE-based method, and SPP-based method. It is clear from experimental results that the proposed algorithm exhibits more excellent noise tracking capability under various nonstationary noise environments and SNR levels. When employed in a speech enhancement framework, improved speech enhancement performance in terms of the segmental SNR (segSNR) improvements and three objective composite metrics is observed.