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Music intelligence : granular data and prediction of top 10 hit songs
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Kim, Seon Tae and Oh, Joo Hee (2021) Music intelligence : granular data and prediction of top 10 hit songs. Decision Support Systems, 145 . 113535. doi:10.1016/j.dss.2021.113535 ISSN 0167-9236.
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Official URL: https://doi.org/10.1016/j.dss.2021.113535
Abstract
In the music market, superstars significantly dominate the market share, while predicting the top hit songs is notoriously difficult. The music intelligence technology, retrieving and utilizing granular acoustic features of songs, provides opportunities to improve the prediction of top hit songs. Using data on 6209 unique songs that appeared in the weekly Billboard Hot 100 charts from 1998 to 2016, especially acoustic features provided by Spotify, we investigate empirically how the top-10-hit-songs likelihood prediction is improved by acoustic features. We find that some acoustic features (e.g., danceability, happiness, and some metrics of timbre and pitch) significantly improve the model's ability to predict the top-10-hit-songs probability. These results suggest that the granular data, provided by the music intelligence technology, carries a substantial predictive value in the era of online music streaming.
Item Type: | Journal Article | ||||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||
Journal or Publication Title: | Decision Support Systems | ||||||||
Publisher: | Elsevier BV | ||||||||
ISSN: | 0167-9236 | ||||||||
Official Date: | June 2021 | ||||||||
Dates: |
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Volume: | 145 | ||||||||
Article Number: | 113535 | ||||||||
DOI: | 10.1016/j.dss.2021.113535 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
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