Noise Cross Power Spectral Density Estimation Using Spatial Information Controlled Recursive Averaging

摘要

Among various noise reduction methods, dual channel methods have the feature of low cost implementation and acceptable performance. Coherence based methods are simple and effective way for speech enhancement. These methods, however, do not work well when the received noise signals are correlated. Coherence-based methods can be improved when the cross power spectral density (CPSD) of noise is available. In this paper, we propose a two channel noise reduction method using spatial information controlled recursive averaging method for estimating noise CPSD. The noise CPSD is updated by averaging the noisy speech power spectrum using a spatial information dependent smoothing factor, which is adjusted based on a novel target presence probability estimator using both phase difference and magnitude squared coherence information. Experimental results show that the proposed algorithm achieves considerably reduced level of both coherent and incoherent noise without further distorting the desired signal components over the comparative algorithms.