A Higher Order Subspace Algorithm for Multichannel Speech Enhancement

Abstract

In this letter, we propose a tensor factorization approach for multichannel speech enhancement, which is very successful even when the noise level is high. Specifically, we extend the well-known subspace approach to arbitrary orders and present the higher order subspace approach for multichannel speech enhancement. Unlike previous algorithms, the proposed approach constructs a third order tensor from the noisy data and then applies a tensor operation to reduce the noise. Through this it preserves the original data structure and makes full use of the spatial and temporal correlations in the multichannel data. The proposed approach adopts an iterative and step-wise procedure which usually converges in a few iterations. At each step a subspace filter sharing the same form with the conventional subspace approach is updated. Experiments show that it has achieved considerable performance on white Gaussian noise in terms of segmental signal-to-noise ratio improvement. Rapid convergence of the proposed approach is also reported.