Herzog, MartinLepa, SteffenSteffens, JochenSchoenrock, AndreasEgermann, Hauke2017-07-132017-07-132017https://depositonce.tu-berlin.de/handle/11303/6476http://dx.doi.org/10.14279/depositonce-5984This 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.en153 Kognitive Prozesse, Intelligenzaudio brandingGMBImusic brandingmusic information retrievalmusic recommendationmusical semanticsprediction modelrandom forest regressionConference Object