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Dance hit song prediction

Author

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  • HERREMANS, Dorien
  • MARTENS, David
  • SÖRENSEN, Kenneth

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 focusing 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.

Suggested Citation

  • HERREMANS, Dorien & MARTENS, David & SÖRENSEN, Kenneth, 2014. "Dance hit song prediction," Working Papers 2014003, University of Antwerp, Faculty of Business and Economics.
  • Handle: RePEc:ant:wpaper:2014003
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    References listed on IDEAS

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    1. Bart Baesens & Rudy Setiono & Christophe Mues & Jan Vanthienen, 2003. "Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation," Management Science, INFORMS, vol. 49(3), pages 312-329, March.
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    Cited by:

    1. Nicola Montecchio & Pierre Roy & François Pachet, 2020. "The skipping behavior of users of music streaming services and its relation to musical structure," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-16, September.

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    More about this item

    Keywords

    Data mining; Classification; Prediction; Music Information Retrieval (MIR);
    All these keywords.

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

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