Noise Eigenvalue Modification Methods for Spatial Subspace Based Multi-Channel Speech Enhancement
In this paper, frequency domain multi-channel filtering schemes are proposed for speech enhancement, based on the subspace decomposition of spatial spectral matrices. For better estimation of noise statistics, which is important for most speech enhancers, we propose noise eigenvalue modification methods for the correction of noise spatial spectral matrix. These methods are based on the rank-1 property of the speech spatial spectral matrix for single desired speech source. Simulation results show that the proposed methods yield better performance compared to the conventional multi-channel Wiener filtering.