A Hybrid of Deep Audio Feature and i-vector for Artist Recognition

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dc.contributor.authorPark, Jiyoungko
dc.contributor.authorKim, Donghyunko
dc.contributor.authorLee, Jongpilko
dc.contributor.authorKeum, Sangeunko
dc.contributor.authorNam, Juhanko
dc.date.accessioned2019-01-23T06:05:35Z-
dc.date.available2019-01-23T06:05:35Z-
dc.date.created2018-12-19-
dc.date.issued2018-07-14-
dc.identifier.citationInternational Conference on Machine Learning(ICML)-
dc.identifier.urihttp://hdl.handle.net/10203/249899-
dc.description.abstractArtist recognition is a task of modeling the artist’s musical style. This problem is challenging because there is no clear standard. We propose a hybrid method of the generative model i-vector and the discriminative model deep convolutional neural network. We show that this approach achieves state-of-the-art performance by complementing each other. In addition, we briefly explain the advantages and disadvantages of each approach.-
dc.languageEnglish-
dc.publisherThe International Machine Learning Society-
dc.titleA Hybrid of Deep Audio Feature and i-vector for Artist Recognition-
dc.typeConference-
dc.type.rimsCONF-
dc.citation.publicationnameInternational Conference on Machine Learning(ICML)-
dc.identifier.conferencecountrySW-
dc.identifier.conferencelocationStockholm International Fairs-
dc.contributor.localauthorNam, Juhan-
dc.contributor.nonIdAuthorPark, Jiyoung-
dc.contributor.nonIdAuthorKim, Donghyun-
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GCT-Conference Papers(학술회의논문)
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