Institutional Repository of Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
| SpecMNet: Spectrum mend network for monaural speech enhancement | |
Fan, Cunhang1 ; Zhang, Hongmei1; Yi, Jiangyan3; Lv, Zhao1,3; Tao, Jianhua3,4; Li, Taihao2; Pei, Guanxiong2; Wu, Xiaopei1; Li, Sheng5
| |
| Source Publication | APPLIED ACOUSTICS
 |
| ISSN | 0003-682X |
| 2022-06-15 | |
| Volume | 194Pages:9 |
| Corresponding Author | Yi, Jiangyan(jiangyan.yi@nlpr.ia.ac.cn) ; Lv, Zhao(kjlz@ahu.edu.cn) ; Tao, Jianhua(jhtao@nlpr.ia.ac.cn) |
| Abstract | 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. |
| Keyword | Monaural speech enhancement Speech distortion Spectrum mend network SI-SNR BLSTM |
| DOI | 10.1016/j.apacoust.2022.108792 |
| WOS Keyword | NEURAL-NETWORK ; NOISE |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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] |
| Funding Organization | 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 |
| WOS Research Area | Acoustics |
| WOS Subject | Acoustics |
| WOS ID | WOS:000798344800011 |
| Publisher | ELSEVIER SCI LTD |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/49552 |
| Collection | 模式识别国家重点实验室_智能交互 |
| Corresponding Author | Yi, Jiangyan; Lv, Zhao; Tao, Jianhua |
| Affiliation | 1.Anhui Univ, Sch Comp Sci & Technol, Anhui Prov Key Lab Multimodal Cognit Computat, Hefei 230601, Peoples R China 2.Artificial Intelligence Res Inst, Zhejiang Lab, Hangzhou 311121, Peoples R China 3.Chinese Acad Sci, Inst Automat, NLPR, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 5.Natl Inst Informat & Commun Technol, Kyoto, Japan |
| Corresponding Author Affilication | Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| Recommended Citation 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. |
| Files in This Item: | There are no files associated with this item. | |||||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment