Title
Dance hit song predictionDance hit song prediction
Author
Faculty/Department
Faculty of Applied Economics
Research group
Engineering Management
Publication type
article
Publication
Lisse,
Subject
Economics
Computer. Automation
Art
Source (journal)
Journal of new music research. - Lisse
Volume/pages
43(2014):3, p. 291-302
ISSN
0929-8215
ISI
000342325300005
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
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
E-info
https://repository.uantwerpen.be/docman/iruaauth/1b86f6/bba2392ecba.pdf
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