Predicting musical meaning in audio branding scenarios

dc.contributor.authorHerzog, Martin
dc.contributor.authorLepa, Steffen
dc.contributor.authorSteffens, Jochen
dc.contributor.authorSchoenrock, Andreas
dc.contributor.authorEgermann, Hauke
dc.date.accessioned2017-07-13T14:13:22Z
dc.date.available2017-07-13T14:13:22Z
dc.date.issued2017
dc.description.abstractThis 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.en
dc.description.sponsorshipEC/H2020/688122/EU/Artist-to-Business-to-Business-to-Consumer Audio Branding System/ABC DJen
dc.identifier.urihttp://depositonce.tu-berlin.de/handle/11303/6476
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-5984
dc.language.isoenen
dc.relation.ispartof10.14279/depositonce-16415en
dc.relation.referenceshttp://dx.doi.org/10.14279/depositonce-5957en
dc.relation.referenceshttp://dx.doi.org/10.14279/depositonce-5958en
dc.relation.referenceshttps://doi.org/10.14279/depositonce-5982
dc.relation.referenceshttps://doi.org/10.14279/depositonce-5983
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subject.ddc153 Kognitive Prozesse, Intelligenzde
dc.subject.otheraudio brandingen
dc.subject.otherGMBIen
dc.subject.othermusic brandingen
dc.subject.othermusic information retrievalen
dc.subject.othermusic recommendationen
dc.subject.othermusical semanticsen
dc.subject.otherprediction modelen
dc.subject.otherrandom forest regressionen
dc.titlePredicting musical meaning in audio branding scenariosen
dc.typeConference Objecten
dc.type.versionsubmittedVersionen
dcterms.bibliographicCitation.proceedingstitleProceedings of the 25th Anniversary Conference of the European Society for Cognitive Science of Music, Ghent, Belgium, 31 July - 4 August 2017en
tub.accessrights.dnbfreeen
tub.affiliationFak. 1 Geistes- und Bildungswissenschaften>Inst. Sprache und Kommunikation>FG Audiokommunikationde
tub.affiliation.facultyFak. 1 Geistes- und Bildungswissenschaftende
tub.affiliation.groupFG Audiokommunikationde
tub.affiliation.instituteInst. Sprache und Kommunikationde
tub.publisher.universityorinstitutionTechnische Universität Berlinen
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