Sequential Deep Neural Networks Ensemble for Speech Bandwidth Extension
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Bong-Ki | - |
dc.contributor.author | Noh, Kyounjin | - |
dc.contributor.author | Chang, Joon-Hyuk | - |
dc.contributor.author | Choo, Kihyun | - |
dc.contributor.author | Oh, Eunmi | - |
dc.date.accessioned | 2021-08-02T13:29:45Z | - |
dc.date.available | 2021-08-02T13:29:45Z | - |
dc.date.created | 2021-05-12 | - |
dc.date.issued | 2018-05 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/16970 | - |
dc.description.abstract | In this paper, we propose a subband-based ensemble of sequential deep neural networks (DNNs) for bandwidth extension (BWE). First, the narrow-band spectra are folded into the high-band (HB) region to generate the high-band spectra, and then the energy levels of the HB spectra are adjusted using the DNN-based on the log-power spectra feature. For this, we basically build the multiple DNNs, which is responsible for each subband of the HB and the DNN ensemble is sequentially connected from lower to higher subbands. This sequential structure for the DNN ensemble carries out the denoising and HB regression to better estimate the HB energy levels. In addition, we use the voiced/unvoiced (V/UV) classification to differently apply the DNN ensemble depending on either V/UV sounds. To demonstrate the performance of the proposed BWE algorithm, we compare it with a speech production model-based BWE system and a DNN-based BWE system in which the log-power spectra in the HB are estimated directly. The experimental results show that the proposed approach provides better speech quality than conventional approaches. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Sequential Deep Neural Networks Ensemble for Speech Bandwidth Extension | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Chang, Joon-Hyuk | - |
dc.identifier.doi | 10.1109/ACCESS.2018.2833890 | - |
dc.identifier.scopusid | 2-s2.0-85046730971 | - |
dc.identifier.wosid | 000435017100001 | - |
dc.identifier.bibliographicCitation | IEEE ACCESS, v.6, pp.27039 - 27047 | - |
dc.relation.isPartOf | IEEE ACCESS | - |
dc.citation.title | IEEE ACCESS | - |
dc.citation.volume | 6 | - |
dc.citation.startPage | 27039 | - |
dc.citation.endPage | 27047 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | BAND EXTENSION | - |
dc.subject.keywordPlus | NARROW-BAND | - |
dc.subject.keywordAuthor | Bandwidth extension | - |
dc.subject.keywordAuthor | sequential deep neural network | - |
dc.subject.keywordAuthor | ensemble | - |
dc.subject.keywordAuthor | log-power spectra | - |
dc.subject.keywordAuthor | regression | - |
dc.subject.keywordAuthor | voiced/unvoiced classification | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/8355864 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.