Microphone Array Speech Enhancement Based on Tensor Filtering Methods  

作　　者：jing wang xiang xie jingming kuang Jing Wang;Xiang Xie;Jingming Kuang(school of information science and technology, beijing institute of technology,Beijing 100081，China)

机构地区：School of Information Science and Technology, Beijing Institute of Technology, Beijing 100081, China

出　　处：《China Communications》2018年第4期141-152,共12页中国通信（英文版）

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

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.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.

关 键 词：speech denoising microphone ar-my tensor filtering truncated HOSVD low rankapproximation multi-mode Wiener filtering 

分 类 号：TN912.35[电子电信—通信与信息系统]