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Main Title: Predicting musical meaning in audio branding scenarios
Author(s): Herzog, Martin
Lepa, Steffen
Steffens, Jochen
Schoenrock, Andreas
Egermann, Hauke
Type: Conference Object
Language Code: en
Abstract: 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.
Issue Date: 2017
Date Available: 13-Jul-2017
DDC Class: 153 Kognitive Prozesse, Intelligenz
Subject(s): audio branding
music branding
music information retrieval
music recommendation
musical semantics
prediction model
random forest regression
Sponsor/Funder: EC/H2020/688122/EU/Artist-to-Business-to-Business-to-Consumer Audio Branding System/ABC DJ
Proceedings Title: Proceedings of the 25th Anniversary Conference of the European Society for Cognitive Science of Music, Ghent, Belgium, 31 July - 4 August 2017
Appears in Collections:FG Audiokommunikation » Publications

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