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광대역 음성에 대한 프레임내 잔차 벡터 양자화에 있어서 모델 복잡도와 성능 사이의 교환관계

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dc.contributor.author송근배-
dc.contributor.author한헌수-
dc.date.available2018-05-10T14:18:28Z-
dc.date.created2018-04-17-
dc.date.issued2010-03-
dc.identifier.issn1975-6291-
dc.identifier.urihttp://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/14866-
dc.description.abstractThis paper addresses a design issue of “model complexity and performance trade-off” in the application of bandwidth extension (BWE) methods to the intra-frame predictive vector quantization problem of wideband speech. It discusses model-based linear and non-linear prediction methods and presents a comparative study of them in terms of prediction gain. Through experimentation, the general trend of saturation in performance (with the increase in model complexity) is observed. However, specifically, it is also observed that there is no significant difference between HMM and GMM-based BWE functions.-
dc.language한국어-
dc.language.isoko-
dc.publisher한국로봇학회-
dc.relation.isPartOf로봇학회 논문지-
dc.subjectBandwidth Extension-
dc.subjectWideband Speech-
dc.subjectGaussian Mixture Model-
dc.subjectHidden Markov Model-
dc.title광대역 음성에 대한 프레임내 잔차 벡터 양자화에 있어서 모델 복잡도와 성능 사이의 교환관계-
dc.title.alternativeTrade-off between Model Complexity and Performance in Intra-frame Predictive Vector Quantization of Wideband Speech-
dc.typeArticle-
dc.type.rimsART-
dc.identifier.bibliographicCitation로봇학회 논문지, v.5, no.1, pp.70 - 76-
dc.identifier.kciidART001421676-
dc.description.journalClass2-
dc.citation.endPage76-
dc.citation.number1-
dc.citation.startPage70-
dc.citation.title로봇학회 논문지-
dc.citation.volume5-
dc.contributor.affiliatedAuthor한헌수-
dc.identifier.urlhttps://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART001421676-
dc.description.isOpenAccessN-
dc.subject.keywordAuthorBandwidth Extension-
dc.subject.keywordAuthorWideband Speech-
dc.subject.keywordAuthorGaussian Mixture Model-
dc.subject.keywordAuthorHidden Markov Model-
dc.description.journalRegisteredClasskci-
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