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A computational model for predicting perceived musical expression in branding scenarios
Citation key lepa_computational_2020
Author Lepa, Steffen and Herzog, Martin and Steffens, Jochen and Schoenrock, Andreas and Egermann, Hauke
Pages 387–402
Year 2020
ISSN 0929-8215
DOI 10.1080/09298215.2020.1778041
Journal Journal of New Music Research
Volume 49
Number 4
Month aug
Note Publisher: Routledge _eprint: https://doi.org/10.1080/09298215.2020.1778041
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.
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