Subspace Noise Estimation and Gamma Distribution Based Microphone Array Post-filter Design

This paper presents a novel method to design the microphone array post-filter. The key issue of post-filter is to accurately estimate the noise power spectrum, thus a subspace based noise estimation method is proposed. Furthermore, the Gamma probability density function is used to describe the signal power spectrum distribution and the signal-plus-noise subspace dimension is determined by maximizing the probability density signal to noise ratio. The noise power spectrum can be computed either with the speech or without the speech, using the eigenvalues corresponding to the noise subspace. With the same Gamma distribution assumption, a post-filter estimation method is proposed. Experiments show that the proposed noise estimation performs better than the conventional VAD based method. The post-filter can obtain a significant gain over the comparing methods in terms of quality measures of the enhanced speech.