Towards automatic music recommendation for audio branding scenarios
dc.contributor.author | Herzog, Martin | |
dc.contributor.author | Lepa, Steffen | |
dc.contributor.author | Egermann, Hauke | |
dc.date.accessioned | 2017-07-13T14:02:29Z | |
dc.date.available | 2017-07-13T14:02:29Z | |
dc.date.issued | 2016 | |
dc.description.abstract | Within the MIR community, most prediction models of musical impact on listeners focus on mood or emotional effects (perceived or induced). The ABC_DJ project investigates the associative impact of music on listeners from the specific perspective of music branding that surrounds us in our everyday lives. We pre-sent a general concept for applying automatic music rec-ommendation within this domain. Creating a scientifically validated basic terminology for communicating brand at-tributes and human emotions in this field is the key chal-lenge. As a first result, we introduce the Music Branding Expert Terminology (MBET), a comprehensive terminology of verbal attributes used in music branding, upon which a pre-diction model will be developed to facilitate automatic mu-sic recommendation in the context of music branding. | en |
dc.description.sponsorship | EC/H2020/688122/EU/Artist-to-Business-to-Business-to-Consumer Audio Branding System/ABC DJ | en |
dc.identifier.uri | https://depositonce.tu-berlin.de/handle/11303/6475 | |
dc.identifier.uri | http://dx.doi.org/10.14279/depositonce-5983 | |
dc.language.iso | en | en |
dc.relation.ispartof | 10.14279/depositonce-16415 | en |
dc.relation.references | http://dx.doi.org/10.14279/depositonce-5957 | en |
dc.relation.references | http://dx.doi.org/10.14279/depositonce-5958 | en |
dc.relation.references | https://doi.org/10.14279/depositonce-5982 | |
dc.relation.references | https://doi.org/10.14279/depositonce-5984 | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en |
dc.subject.ddc | 003 Systeme | de |
dc.subject.other | audio branding | en |
dc.subject.other | music branding | en |
dc.subject.other | MBET | en |
dc.subject.other | MIR | en |
dc.subject.other | music features | en |
dc.title | Towards automatic music recommendation for audio branding scenarios | en |
dc.type | Conference Object | en |
dc.type.version | publishedVersion | en |
dcterms.bibliographicCitation.proceedingstitle | ISMIR 2016, August 7-11, New York City, USA / LBD contributions | en |
tub.accessrights.dnb | free | en |
tub.affiliation | Fak. 1 Geistes- und Bildungswissenschaften::Inst. Sprache und Kommunikation::FG Audiokommunikation | de |
tub.affiliation.faculty | Fak. 1 Geistes- und Bildungswissenschaften | de |
tub.affiliation.group | FG Audiokommunikation | de |
tub.affiliation.institute | Inst. Sprache und Kommunikation | de |
tub.publisher.universityorinstitution | Technische Universität Berlin | en |