Late reverberant power spectral density estimation based on an eigenvalue decomposition

Abstract

Multi-channel methods for estimating the late reverberant power spectral density (PSD) rely on an estimate of the direction of arrival (DOA) of the speech source or of the relative early transfer functions (RETFs) of the target signal from a reference microphone to all microphones. The DOA and the RETFs may be difficult to estimate accurately, particularly in highly reverberant and noisy scenarios. In this paper we propose a novel multi-channel method to estimate the late reverberant PSD which does not require estimates of the DOA or RETFs. The late reverberation is modeled as an isotropic sound field and the late reverberant PSD is estimated based on the eigenvalues of the prewhitened received signal PSD matrix. Experimental results demonstrate the advantages of using the proposed estimator in a multi-channel Wiener filter for speech dereverberation, outperforming a recently proposed maximum likelihood estimator both when the DOA is perfectly estimated as well as in the presence of DOA estimation errors.