Microphone Array Speech Enhancement Based on Tensor Filtering Methods

摘要

This paper proposes a novel micro-phone array speech denoising scheme based on tensor filtering methods including truncated HOSVD (High-Order Singular Value Decom-position), low rank tensor approximation and multi-mode Wiener filtering. Microphone ar-ray speech signal is represented in three-order tensor space with channel, time, and spectrum modes and then tensor filtering model can be designed to process the multiway array data. As to the first method, noise can be reduced through the truncated HOSVD which is a simple scheme in tensor processing. It is more accurate to find the lower-rank approximation of the three-order tensor with Tucker model. Then MDL (Minimum Description Length) criterion is used to estimate the optimal tensor rank in the second method. Further, multi-mode Wiener filtering approach upon tensor analysis can be considered as the spanning of one-mode wiener filtering. How to take advan-tages of tensor model to obtain a set of filters is the heart of the novel scheme. The perfor-mances of the proposed three approaches are evaluated with objective indexes and listening quality test. The experimental results indicate that the proposed tenor filtering methods have potential ability of retrieving the target signal from noisy microphone array signal and the multi-mode Wiener filtering method provides the best denoising results among the three ones.