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dc.contributor.authorKruspe, Anna
dc.contributor.authorZapf, Dominik
dc.contributor.authorLukashevich, Hanna
dc.contributor.editorEibl, Maximilian
dc.contributor.editorGaedke, Martin
dc.date.accessioned2017-08-28T23:46:57Z
dc.date.available2017-08-28T23:46:57Z
dc.date.issued2017
dc.identifier.isbn978-3-88579-669-5
dc.identifier.issn1617-5468
dc.description.abstractAutomatic speech/music discrimination describes the task of automatically detecting speech and music audio within a recording. This is useful for a great number of tasks in both research and industry. In particular, this approach can be used for broadcast signals (e.g. from TV or radio stations) in order to determine the amount of music played. The results can then be used for various reporting purposes (e.g. for royalty collection societies such as the German GEMA). Speech/music discrimination is commonly performed by using machine learning technologies, where models are first trained on manually annotated data, and can then be used to classify previously unseen audio data. In this paper, we give an overview over the applications and the state of the art of speech/music discrimination. Afterwards, we present our approaches based on a set of audio features, Gaussian Mixture Models and Deep Learning. Finally, we give suggestions for the direction of new research into this topic.en
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofINFORMATIK 2017
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-275
dc.subjectspeech/music discrimination
dc.subjectmusic/speech classification
dc.subjectmusic detection
dc.subjectmusic analysis
dc.titleAutomatic speech/music discrimination for broadcast signalsen
mci.reference.pages151-162
mci.conference.sessiontitleMusik trifft Informatik
mci.conference.locationChemnitz
mci.conference.date25.-29. September 2017
dc.identifier.doi10.18420/in2017_10


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