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End-to-End Post-Filter for Speech Separation With Deep Attention Fusion Features

文献类型：期刊论文

作者Fan, Cunhang1,3; Tao, Jianhua1,2,3; Liu, Bin1; Yi, Jiangyan1; Wen, Zhengqi1; Liu, Xuefei1
刊名IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING
出版日期2020
卷号28页码:1303-1314
关键词Feature extraction Training Interference Speech enhancement Clustering algorithms Spectrogram Speech separation end-to-end post-filter deep attention fusion features deep clustering permutation invariant training
ISSN号2329-9290
DOI10.1109/TASLP.2020.2982029
通讯作者Tao, Jianhua(jhtao@nlpr.ia.ac.cn) ; Liu, Bin(liubin@nlpr.ia.ac.cn)
英文摘要In this article, we propose an end-to-end post-filter method with deep attention fusion features for monaural speaker-independent speech separation. At first, a time-frequency domain speech separation method is applied as the pre-separation stage. The aim of pre-separation stage is to separate the mixture preliminarily. Although this stage can separate the mixture, it still contains the residual interference. In order to enhance the pre-separated speech and improve the separation performance further, the end-to-end post-filter (E2EPF) with deep attention fusion features is proposed. The E2EPF can make full use of the prior knowledge of the pre-separated speech, which contributes to speech separation. It is a fully convolutional speech separation network and uses the waveform as the input features. Firstly, the 1-D convolutional layer is utilized to extract the deep representation features for the mixture and pre-separated signals in the time domain. Secondly, to pay more attention to the outputs of the pre-separation stage, an attention module is applied to acquire deep attention fusion features, which are extracted by computing the similarity between the mixture and the pre-separated speech. These deep attention fusion features are conducive to reduce the interference and enhance the pre-separated speech. Finally, these features are sent to the post-filter to estimate each target signals. Experimental results on the WSJ0-2mix dataset show that the proposed method outperforms the state-of-the-art speech separation method. Compared with the pre-separation method, our proposed method can acquire 64.1%, 60.2%, 25.6% and 7.5% relative improvements in scale-invariant source-to-noise ratio (SI-SNR), the signal-to-distortion ratio (SDR), the perceptual evaluation of speech quality (PESQ) and the short-time objective intelligibility (STOI) measures, respectively.
WOS关键词NETWORK
资助项目National Key Research and Development Plan of China[2017YFC0820602] ; National Natural Science Foundation of China (NSFC)[61831022] ; National Natural Science Foundation of China (NSFC)[61771472] ; National Natural Science Foundation of China (NSFC)[61901473] ; National Natural Science Foundation of China (NSFC)[61773379] ; Inria-CAS Joint Research Project[173211KYSB20170061] ; Inria-CAS Joint Research Project[173211KYSB20190049]
WOS研究方向Acoustics ; Engineering
语种英语
WOS记录号WOS:000536055600001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Key Research and Development Plan of China ; National Natural Science Foundation of China (NSFC) ; Inria-CAS Joint Research Project
源URL[http://ir.ia.ac.cn/handle/173211/39516]  
专题模式识别国家重点实验室_智能交互
通讯作者Tao, Jianhua; Liu, Bin
作者单位1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Fan, Cunhang,Tao, Jianhua,Liu, Bin,et al. End-to-End Post-Filter for Speech Separation With Deep Attention Fusion Features[J]. IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING,2020,28:1303-1314.
APA Fan, Cunhang,Tao, Jianhua,Liu, Bin,Yi, Jiangyan,Wen, Zhengqi,&Liu, Xuefei.(2020).End-to-End Post-Filter for Speech Separation With Deep Attention Fusion Features.IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING,28,1303-1314.
MLA Fan, Cunhang,et al."End-to-End Post-Filter for Speech Separation With Deep Attention Fusion Features".IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 28(2020):1303-1314.

入库方式： OAI收割

来源：自动化研究所

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