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Predicting musical meaning in audio branding scenarios
Herzog, Martin; Lepa, Steffen; Steffens, Jochen; Schoenrock, Andreas; Egermann, Hauke
This paper describes the concept of applying automatic music recommendation to the audio branding domain. We describe our approach of developing a prediction model for the perceived expressive content of music which is based on a large-scale listening experiment. We present an orthogonal 4-factor model for measuring musical expression as outcome variable, whereas audio- and music features as well as lyric-based features are introduced as prediction variables in the model. Furthermore, we describe Random Forest Regression as a concept for feature selection required to develop a Multi-Level Regression Model, which is taking individual listener parameters into account. Finally, we present first results from a preliminary stepwise regression model for perceived musical expression.
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Published in: Proceedings of the 25th Anniversary Conference of the European Society for Cognitive Science of Music, Ghent, Belgium, 31 July - 4 August 2017,
