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Towards Classification of Technical Sound Events with Deep Learning Models

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dc.contributor.authorRieder, Constantin
dc.contributor.authorGermann, Markus
dc.contributor.authorScherer, Klaus Peter
dc.contributor.editorHeisig, Peter
dc.contributor.editorOrth, Ronald
dc.contributor.editorSchönborn, Jakob Michael
dc.contributor.editorThalmann, Stefan
dc.date.accessioned2020-10-05T11:30:35Z
dc.date.available2020-10-05T11:30:35Z
dc.date.issued2020
dc.description.abstractSounds of machines and mechanical systems contain a lot of information about the observed object and its state. Experienced engineers and technical service staff can often identify or classify a certain technical object with state via its sound. An equivalent automated system with such capabilities is difficult to realise because of noisy unknown surroundings. In this paper, we show an approach to implement the mentioned characteristics with deep learning methods and enhance the power of a technical assistance system.de
dc.identifier.isbn978-3-88579-607-8
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/34383
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofWM 2019 - Wissensmanagement in digitalen Arbeitswelten: Aktuelle Ansätze und Perspektiven - Knowledge Management in Digital Workplace Environments: State of the Art and Outlook
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-303
dc.subjectDeep Learning
dc.subjectSound Analysis
dc.subjectInformation Systems
dc.titleTowards Classification of Technical Sound Events with Deep Learning Modelsde
dc.typeText/Conference Paper
gi.citation.endPage193
gi.citation.publisherPlaceBonn
gi.citation.startPage188
gi.conference.date18.-20. März 2019
gi.conference.locationPotsdam
gi.conference.sessiontitleWS IV: Data-Driven Knowledge Management

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P303 - WM 2019 - Wissensmanagement in digitalen Arbeitswelten: Aktuelle Ansätze und Perspektiven - Knowledge Management in Digital Workplace Environments: State of the Art and Outlook