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dc.contributor.authorHerremans, Den_US
dc.contributor.authorMartens, Den_US
dc.contributor.authorSörensen, Ken_US
dc.date.accessioned2016-07-21T07:38:41Z
dc.date.issued2014-07-03en_US
dc.date.submitted2016-04-04T15:28:28.507Z
dc.identifier.issn0929-8215en_US
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/13590
dc.description.abstract© 2014, © 2014 Taylor & Francis. Record companies invest billions of dollars in new talent around the globe each year. Gaining insight into what actually makes a hit song would provide tremendous benefits for the music industry. In this research we tackle this question by focussing on the dance hit song classification problem. A database of dance hit songs from 1985 until 2013 is built, including basic musical features, as well as more advanced features that capture a temporal aspect. A number of different classifiers are used to build and test dance hit prediction models. The resulting best model has a good performance when predicting whether a song is a ‘top 10’ dance hit versus a lower listed position.en_US
dc.description.sponsorshipThis research has been partially supported by the Interuniversity Attraction Poles (IUAP) Programme initiated by the Belgian Science Policy Office (COMEX project).en_US
dc.format.extent291 - 302en_US
dc.relation.ispartofJournal of New Music Researchen_US
dc.rightsThis is an Accepted Manuscript of an article published by Taylor & Francis Group in Journal of New Music Research on 03/07/2014, available online: http://dx.doi.org/10.1080/09298215.2014.881888
dc.titleDance Hit Song Predictionen_US
dc.typeArticle
dc.rights.holder© 2014 Taylor & Francis
dc.identifier.doi10.1080/09298215.2014.881888en_US
pubs.issue3en_US
pubs.notesNot knownen_US
pubs.notesInitial upload not completed by author, 04/04/2016; completed on behalf of the author, 14/07/2016, SMen_US
pubs.publication-statusPublisheden_US
pubs.volume43en_US


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