A computational model for predicting perceived musical expression in branding scenarios. Issue 4 (7th August 2020)

• Record Type: Journal Article
• Authors: Lepa, Steffen; Herzog, Martin; Steffens, Jochen; Schoenrock, Andreas; Egermann, Hauke
• Is Part Of: Journal of new music research. Volume 49:Issue 4(2020)
• Page Start: 387
• Page End: 402
• Publication Date: 2020-08-07
• Subjects: Music information retrieval -- listener modelling -- recommendation systems -- audio branding -- machine learning; Music -- Periodicals; Musicology -- Periodicals; 780.72
• DOI: 10.1080/09298215.2020.1778041
• Languages: English
• ISSNs: 0929-8215

Abstract

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.