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End-to-End Post-Filter for Speech Separation With Deep Attention Fusion Features | |
Fan, Cunhang1,2![]() ![]() ![]() ![]() ![]() | |
发表期刊 | IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING
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ISSN | 2329-9290 |
2020 | |
卷号 | 28期号:28页码:1303-1314 |
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. |
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 |
DOI | 10.1109/TASLP.2020.2982029 |
关键词[WOS] | NETWORK |
收录类别 | SCI |
语种 | 英语 |
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] |
项目资助者 | National Key Research and Development Plan of China ; National Natural Science Foundation of China (NSFC) ; Inria-CAS Joint Research Project |
WOS研究方向 | Acoustics ; Engineering |
WOS类目 | Acoustics ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000536055600001 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 语音识别与合成 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/39517 |
专题 | 多模态人工智能系统全国重点实验室_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.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China 3.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
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(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(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 28.28(2020):1303-1314. |
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