Musical Word Embedding: Bridging the Gap between Listening Contexts and Music

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dc.contributor.authorDoh, Seungheonko
dc.contributor.authorLEE, JONGPILko
dc.contributor.authorPark, Tae Hongko
dc.contributor.authorNam, Juhanko
dc.date.accessioned2021-01-28T06:09:46Z-
dc.date.available2021-01-28T06:09:46Z-
dc.date.created2020-11-27-
dc.date.issued2020-07-18-
dc.identifier.citationMachine Learning for Media Discovery Workshop (ML4DL), International Conference on Machine Learning-
dc.identifier.urihttp://hdl.handle.net/10203/280168-
dc.description.abstractWord embedding pioneered by Mikolov et al. is a staple technique for word representations in natural language processing (NLP) research which has also found popularity in music information retrieval tasks. Depending on the type of text data for word embedding, however, vocabulary size and the degree of musical pertinence can significantly vary. In this work, we (1) train the distributed representation of words using combinations of both general text data and music-specific data and (2) evaluate the system in terms of how they associate listening contexts with musical compositions.-
dc.languageEnglish-
dc.publisherThe International Conference on Machine Learning (ICML)-
dc.titleMusical Word Embedding: Bridging the Gap between Listening Contexts and Music-
dc.typeConference-
dc.type.rimsCONF-
dc.citation.publicationnameMachine Learning for Media Discovery Workshop (ML4DL), International Conference on Machine Learning-
dc.identifier.conferencecountryAU-
dc.identifier.conferencelocationVirtual-
dc.contributor.localauthorNam, Juhan-
dc.contributor.nonIdAuthorDoh, Seungheon-
dc.contributor.nonIdAuthorPark, Tae Hong-
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GCT-Conference Papers(학술회의논문)
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