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dc.contributor.authorWohlfahrt-Laymann, Jan
dc.contributor.authorHeimbürger, Anneli
dc.date.accessioned2017-11-21T09:47:23Z
dc.date.available2017-11-21T09:47:23Z
dc.date.issued2017
dc.identifier.citationWohlfahrt-Laymann, J., & Heimbürger, A. (2017). Content Aware Music Analysis with Multi-Dimensional Similarity Measure. In H. Jaakkola, B. Thalheim, Y. Kiyoki, & N. Yoshida (Eds.), <em>Information Modelling and Knowledge Bases XXVIII</em> (pp. 303-313). Frontiers in Artificial Intelligence and Applications, 292. IOS Press. <a href="https://doi.org/10.3233/978-1-61499-720-7-303">doi:10.3233/978-1-61499-720-7-303</a>
dc.identifier.otherTUTKAID_72292
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/55954
dc.description.abstractMusic players and cloud solution for music recommendation and automatic playlist creation are becoming increasingly more popular, as they intent to overcome the issue of the difficulty for users to find fitting music, based on context, mood and impression. Much research on the topic has been conducted, which has recommended different approaches to overcome this problem. This paper suggests a system which uses a multi-dimensional vector space, based on the music’s key elements, as well as the mood expressed through them and the song lyrics, which allows for difference and similarity finding to automatically generate a contextually meaningful playlist.
dc.format.extent483
dc.language.isoeng
dc.publisherIOS Press
dc.relation.ispartofInformation Modelling and Knowledge Bases XXVIII, ISBN 978-1-61499-719-1
dc.relation.ispartofseriesFrontiers in Artificial Intelligence and Applications;292
dc.subject.otherrecommender systems
dc.subject.othermusic analysis
dc.subject.otherautomatic playlist creation
dc.titleContent Aware Music Analysis with Multi-Dimensional Similarity Measure
dc.typebookPart
dc.identifier.urnURN:NBN:fi:jyu-201711174283
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikka
dc.type.urihttp://purl.org/eprint/type/BookItem
dc.date.updated2017-11-17T13:15:12Z
dc.relation.isbn978-1-61499-719-1
dc.type.coarbook part
dc.description.reviewstatuspeerReviewed
dc.format.pagerange303-313
dc.relation.issn0922-6389
dc.type.versionacceptedVersion
dc.rights.copyright© IOS Press, 2017. This is a final draft version of an article whose final and definitive form has been published by IOS Press. Published in this repository with the kind permission of the publisher.
dc.rights.accesslevelopenAccessfi
dc.relation.doi10.3233/978-1-61499-720-7-303


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