Deep neural network ensemble for reducing artificial noise in bandwidth extension
DC Field | Value | Language |
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dc.contributor.author | Noh, Kyoungjin | - |
dc.contributor.author | Chang, Joon Hyuk | - |
dc.date.accessioned | 2021-07-30T04:54:40Z | - |
dc.date.available | 2021-07-30T04:54:40Z | - |
dc.date.created | 2021-05-12 | - |
dc.date.issued | 2020-07 | - |
dc.identifier.issn | 1051-2004 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/2036 | - |
dc.description.abstract | In this paper, we propose a deep neural network (DNN) ensemble for reducing artificial noise in speech bandwidth extension (BWE). The proposed DNN ensemble consists of three DNN models; one is a classification model, and the other two are regression models. When estimating sub-band energies of the high-frequency region using sequential DNNs in a frequency domain, the over-estimation of sub-band energies causes annoying artificial noise. To mitigate this artificial noise, we design a DNN classification model that can classify over-estimation frames against normal frames. Then, we separately develop two DNN regression models using half of the entire training set and a limited training set built with overestimation frames and some normal frames to improve the performance at the over-estimation frames. Since the outputs of the classification model are probabilities of either a normal frame or an overestimation frame, respectively, two regression models are adjustably combined by using the probabilistic weights; thus, the final output of the DNN ensemble is the weighted sum of two estimated sub-band energies. As a result, artificial noise is significantly reduced, yielding improved speech quality. The proposed method is objectively and subjectively evaluated by comparing it with conventional approaches. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | ACADEMIC PRESS INC ELSEVIER SCIENCE | - |
dc.title | Deep neural network ensemble for reducing artificial noise in bandwidth extension | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Chang, Joon Hyuk | - |
dc.identifier.doi | 10.1016/j.dsp.2020.102760 | - |
dc.identifier.scopusid | 2-s2.0-85084373039 | - |
dc.identifier.wosid | 000536136500008 | - |
dc.identifier.bibliographicCitation | DIGITAL SIGNAL PROCESSING, v.102, pp.1 - 6 | - |
dc.relation.isPartOf | DIGITAL SIGNAL PROCESSING | - |
dc.citation.title | DIGITAL SIGNAL PROCESSING | - |
dc.citation.volume | 102 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 6 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | Bandwidth | - |
dc.subject.keywordPlus | Frequency domain analysis | - |
dc.subject.keywordPlus | Frequency estimation | - |
dc.subject.keywordPlus | Regression analysis | - |
dc.subject.keywordPlus | Deep neural networks | - |
dc.subject.keywordPlus | Artificial noise | - |
dc.subject.keywordPlus | Bandwidth extension | - |
dc.subject.keywordPlus | Classification models | - |
dc.subject.keywordPlus | Conventional approach | - |
dc.subject.keywordPlus | Frequency domains | - |
dc.subject.keywordPlus | High frequency HF | - |
dc.subject.keywordPlus | Neural network ensembles | - |
dc.subject.keywordPlus | Regression model | - |
dc.subject.keywordAuthor | Bandwidth extension | - |
dc.subject.keywordAuthor | Deep neural network | - |
dc.subject.keywordAuthor | Ensemble | - |
dc.subject.keywordAuthor | Artificial noise | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S1051200420301056?via%3Dihub | - |
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