Microphone Array Speech Enhancement Based on Tensor Filtering Methods

作     者：jing wang xiang xie jingming kuang Jing Wang;Xiang Xie;Jingming Kuang

作者机构：School of Information Science and Technology Beijing Institute of Technology Beijing 100081 China 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2018年第15卷第4期

页      面：141-152页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 

基　　金：supported by the National Natural Science Foundation of China(No.61571044 No.11590772 No.61473041 and No.61620106002) 

主　　题：speech denoising microphone ar-my tensor filtering truncated HOSVD low rankapproximation multi-mode Wiener filtering 

摘      要：This paper proposes a novel microphone array speech denoising scheme based on tensor filtering methods including truncated HOSVD(High-Order Singular Value Decomposition), low rank tensor approximation and multi-mode Wiener filtering. Microphone array 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, multimode Wiener filtering approach upon tensor analysis can be considered as the spanning of one-mode wiener filtering. How to take advantages of tensor model to obtain a set of filters is the heart of the novel scheme. The performances 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.