Lepa, Steffen, Herzog, Martin, Steffens, Jochen et al. (2 more authors) (2020) A computational model for predicting perceived musical expression in branding scenarios. Journal of New Music Research. ISSN 0929-8215
Abstract
We describe the development of a computational model predicting listener-perceived expressions of music in branding contexts. Representative ground truth from multi-national online listening experiments was combined with machine learning of music branding expert knowledge, and audio signal analysis toolbox outputs. A mixture of random forest and traditional regression models is able to predict average ratings of perceived brand image on four dimensions. Resulting cross-validated prediction accuracy (R²) was Arousal: 61%, Valence: 44%, Authenticity: 55%, and Timeliness: 74%. Audio descriptors for rhythm, instrumentation, and musical style contributed most. Adaptive sub-models for different marketing target groups further increase prediction accuracy.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020, Informa UK Limited, trading as Taylor & Francis Group. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Arts and Humanities (York) > Music (York) |
Depositing User: | Pure (York) |
Date Deposited: | 09 Jul 2020 14:00 |
Last Modified: | 26 Nov 2024 00:47 |
Published Version: | https://doi.org/10.1080/09298215.2020.1778041 |
Status: | Published online |
Refereed: | Yes |
Identification Number: | 10.1080/09298215.2020.1778041 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:163011 |
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