Herzog, Martin, Lepa, Steffen, Steffens, Jochen et al. (2 more authors) (2017) Predicting Musical Meaning in Audio Branding Scenarios. In: Van Dyck, E., (ed.) Proceedings of the 25th Anniversary Conference of the European Society for Cognitive Science of Music, Ghent, Belgium, 31 July – 4 August 2017. European Society for Cognitive Sciences Of Music Conference, 31 Jul - 04 Aug 2017 ESCOM , BEL , pp. 75-79.
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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Keywords: | Music Branding,Audio Branding,Musical Semantics,Music Information Retrieval |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Arts and Humanities (York) > Music (York) |
Depositing User: | Pure (York) |
Date Deposited: | 18 May 2017 08:00 |
Last Modified: | 17 Dec 2024 00:33 |
Status: | Published |
Publisher: | ESCOM |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:116600 |
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