Projects per year
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.
Original language | English |
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Journal | Journal of New Music Research |
Early online date | 16 Jun 2020 |
DOIs | |
Publication status | E-pub ahead of print - 16 Jun 2020 |
Bibliographical note
© 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.Projects
- 1 Finished
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ABC_DJ: Artist-to-Business-to-Business-to-Consumer Audio Branding System
1/01/16 → 31/12/18
Project: Research project (funded) › Research