Publication
Title
Dance hit song prediction
Author
Abstract
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. Keywords: machine learning, databases, information retrieval, music analysis
Language
English
Source (journal)
Journal of new music research. - Lisse
Publication
Lisse : 2014
ISSN
0929-8215
Volume/pages
43:3(2014), p. 291-302
ISI
000342325300005
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
[E?say:metaLocaldata.cgzprojectinf]
Publication type
Subject
Art 
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 15.09.2014
Last edited 21.09.2017
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