By the same authors

Predicting Musical Meaning in Audio Branding Scenarios

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Publication details

Title of host publicationProceedings of the 25th Anniversary Conference of the European Society for Cognitive Science of Music, Ghent, Belgium, 31 July – 4 August 2017
DateAccepted/In press - 23 Dec 2016
DatePublished (current) - 31 Jul 2017
Pages75-79
Number of pages5
PublisherESCOM
EditorsE. Van Dyck
Original languageEnglish

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.

    Research areas

  • Music Branding, Audio Branding, Musical Semantics, Music Information Retrieval

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