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SpecMNet: Spectrum mend network for monaural speech enhancement

文献类型：期刊论文

作者Fan, Cunhang5; Zhang, Hongmei5; Yi, Jiangyan3; Lv, Zhao3,5; Tao, Jianhua2,3; Li, Taihao4; Pei, Guanxiong4; Wu, Xiaopei5; Li, Sheng1
刊名APPLIED ACOUSTICS
出版日期2022-06-15
卷号194页码:9
关键词Monaural speech enhancement Speech distortion Spectrum mend network SI-SNR BLSTM
ISSN号0003-682X
DOI10.1016/j.apacoust.2022.108792
通讯作者Yi, Jiangyan(jiangyan.yi@nlpr.ia.ac.cn) ; Lv, Zhao(kjlz@ahu.edu.cn) ; Tao, Jianhua(jhtao@nlpr.ia.ac.cn)
英文摘要Speech enhancement methods usually suffer from speech distortion problem, which leads to the enhanced speech losing so much significant speech information. This damages the speech quality and intelligibility. In order to address this issue, we propose a spectrum mend network (SpecMNet) for monaural speech enhancement. The proposed SpecMNet aims to retrieve the lost information by mending the weighted enhanced spectrum with weighted original spectrum. More specifically, the proposed algorithm consists of pre-enhancement network and the mend network. The main task of preenhancement network is to acquire the pre-enhanced spectrum so that it can remove the most of the noise signals. Because of the speech distortion problem, it loses a great deal of speech components. While the original spectrum has no speech information lost. Therefore, we utilize the original spectrum to mend the pre-enhanced spectrum by adding these two weighted spectrums so that the lost speech information can be retrieved. Then the mend network is used to predict mend weights for these two spectrums. Finally, the mended spectrum is used as the enhanced output. Our experiments are conducted on the TIMIT + (100 Nonspeech Sounds and NOISEX-92) datasets. Experimental results demonstrate that our proposed SpecMNet approach is effective to alleviate the speech distortion problem. (c) 2022 Elsevier Ltd. All rights reserved.
WOS关键词NEURAL-NETWORK ; NOISE
资助项目National Key Research and Development Program of China[2021ZD0201502] ; National Natural Science Foundation of China (NSFC)[61972437] ; Open Research Projects of Zhejiang Lab[2021KH0AB06] ; Open Projects Program of National Laboratory of Pattern Recognition[202200014]
WOS研究方向Acoustics
语种英语
WOS记录号WOS:000798344800011
出版者ELSEVIER SCI LTD
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China (NSFC) ; Open Research Projects of Zhejiang Lab ; Open Projects Program of National Laboratory of Pattern Recognition
源URL[http://ir.ia.ac.cn/handle/173211/49551]  
专题模式识别国家重点实验室_智能交互
通讯作者Yi, Jiangyan; Lv, Zhao; Tao, Jianhua
作者单位1.Natl Inst Informat & Commun Technol, Kyoto, Japan
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Automat, NLPR, Beijing 100190, Peoples R China
4.Artificial Intelligence Res Inst, Zhejiang Lab, Hangzhou 311121, Peoples R China
5.Anhui Univ, Sch Comp Sci & Technol, Anhui Prov Key Lab Multimodal Cognit Computat, Hefei 230601, Peoples R China
推荐引用方式
GB/T 7714		 Fan, Cunhang,Zhang, Hongmei,Yi, Jiangyan,et al. SpecMNet: Spectrum mend network for monaural speech enhancement[J]. APPLIED ACOUSTICS,2022,194:9.
APA Fan, Cunhang.,Zhang, Hongmei.,Yi, Jiangyan.,Lv, Zhao.,Tao, Jianhua.,...&Li, Sheng.(2022).SpecMNet: Spectrum mend network for monaural speech enhancement.APPLIED ACOUSTICS,194,9.
MLA Fan, Cunhang,et al."SpecMNet: Spectrum mend network for monaural speech enhancement".APPLIED ACOUSTICS 194(2022):9.

入库方式： OAI收割

来源：自动化研究所

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