Zhao, M., Harvey, M. orcid.org/0000-0001-5504-2089, Cameron, D. orcid.org/0000-0001-8923-5591 et al. (2 more authors) (2023) An analysis of classification approaches for hit song prediction using engineered metadata features with lyrics and audio features. In: Sserwanga, I., Goulding, A., Moulaison-Sandy, H., Du, J.T., Soares, A.L., Hessami, V. and Frank, R.D., (eds.) Information for a Better World: Normality, Virtuality, Physicality, Inclusivity: 18th International Conference, iConference 2023, Virtual Event, March 13–17, 2023, Proceedings, Part I. 18th International Conference, iConference 2023, 13-17 Mar 2023, Virtual Event. Lecture Notes in Computer Science, LNCS 13971 . Springer Nature Switzerland , pp. 303-311. ISBN 9783031280344
Abstract
Hit song prediction, one of the emerging fields in music information retrieval (MIR), remains a considerable challenge. Being able to understand what makes a given song a hit is clearly beneficial to the whole music industry. Previous approaches to hit song prediction have focused on using audio features of a record. This study aims to improve the prediction result of the top 10 hits among Billboard Hot 100 songs using more alternative metadata, including song audio features provided by Spotify, song lyrics, and novel metadata-based features (title topic, popularity continuity and genre class). Five machine learning approaches are applied, including: k-nearest neighbours, Naïve Bayes, Random Forest, Logistic Regression and Multilayer Perceptron. Our results show that Random Forest (RF) and Logistic Regression (LR) with all features (including novel features, song audio features and lyrics features) outperforms other models, achieving 89.1% and 87.2% accuracy, and 0.91 and 0.93 AUC, respectively. Our findings also demonstrate the utility of our novel music metadata features, which contributed most to the models’ discriminative performance.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG. This is an author-produced version of a paper subsequently published in Information for a Better World: Normality, Virtuality, Physicality, Inclusivity: 18th International Conference, iConference 2023, Virtual Event, March 13–17, 2023, Proceedings, Part I, Lecture Notes in Computer Science. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Hit song prediction; Music information retrieval; Machine learning; Text processing |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 14 Sep 2023 15:42 |
Last Modified: | 10 Mar 2024 01:13 |
Status: | Published |
Publisher: | Springer Nature Switzerland |
Series Name: | Lecture Notes in Computer Science |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-031-28035-1_21 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:203203 |