A computational model for predicting perceived musical expression in branding scenarios

Steffen Lepa, Martin Herzog, Jochen Steffens, Andreas Schoenrock, Hauke Egermann

Research output: Contribution to journalArticlepeer-review


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 languageEnglish
JournalJournal of New Music Research
Early online date16 Jun 2020
Publication statusE-pub ahead of print - 16 Jun 2020

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