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Content-based Recommendations for Radio Stations with Deep Learned Audio Fingerprints

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dc.contributor.authorLanger, Stefan
dc.contributor.authorObermeier, Liza
dc.contributor.authorEbert, André
dc.contributor.authorFriedrich, Markus
dc.contributor.authorMunisamy, Emma
dc.contributor.editorReussner, Ralf H.
dc.contributor.editorKoziolek, Anne
dc.contributor.editorHeinrich, Robert
dc.date.accessioned2021-01-27T13:33:44Z
dc.date.available2021-01-27T13:33:44Z
dc.date.issued2021
dc.description.abstractThe world of linear radio broadcasting is characterized by a wide variety of stations and played content. That is why finding stations playing the preferred content is a tough task for a potential listener, especially due to the overwhelming number of offered choices. Here, recommender systems usually step in but existing content-based approaches rely on metadata and thus are constrained by the available data quality. Other approaches leverage user behavior data and thus do not exploit any domain-specific knowledge and are furthermore disadvantageous regarding privacy concerns. Therefore, we propose a new pipeline for the generation of audio-based radio station fingerprints relying on audio stream crawling and a Deep Autoencoder. We show that the proposed fingerprints are especially useful for characterizing radio stations by their audio content and thus are an excellent representation for meaningful and reliable radio station recommendations. Furthermore, the proposed modules are part of the HRADIO Communication Platform, which enables hybrid radio features to radio stations. It is released with a flexible open source license and enables especially small-and medium-sized businesses, to provide customized and high quality radio services to potential listeners.en
dc.identifier.doi10.18420/inf2020_38
dc.identifier.isbn978-3-88579-701-2
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/34746
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofINFORMATIK 2020
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-307
dc.subjectHybrid Radio
dc.subjectMultimedia Services
dc.subjectRecommender Systems
dc.subjectUnsupervised Learning
dc.subjectDeep Audio Fingerprints
dc.subjectDeep Learning
dc.titleContent-based Recommendations for Radio Stations with Deep Learned Audio Fingerprintsen
gi.citation.endPage425
gi.citation.startPage411
gi.conference.date28. September - 2. Oktober 2020
gi.conference.locationKarlsruhe
gi.conference.sessiontitleKünstliche Intelligenz für kleine und mittlere Unternehmen

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P307 - INFORMATIK 2020 - Back to the Future