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SRAD-CLF: Squeak and Rattle Anomaly Detection via Contrastive Learning Framework on Real Industrial Noise Recordings

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dc.contributor.authorLiu, Zhaoyi
dc.contributor.authorLopez-Chilet, Alvaro
dc.contributor.authorChong, Dading
dc.contributor.authorMichiels, Sam
dc.contributor.authorGomez, Jon Ander
dc.contributor.authorWolf-Monheim, Friedrich
dc.contributor.authorNewton, David
dc.contributor.authorHughes, Danny
dc.date.accessioned2024-12-21T17:09:31Z
dc.date.available2024-12-21T17:09:31Z
dc.date.issued2024
dc.description.wosFundingTextFunded by the Research Fund KU Leuven (ReSOS project C3/20/014) and Ford Motor Company (Ford-KU Leuven Research Alliance, Automated S&R project KUL0134).
dc.identifier.eisbn978-9-4645-9361-7
dc.identifier.isbn979-8-3315-1977-3
dc.identifier.issn2076-1465
dc.identifier.urihttps://imec-publications.be/handle/20.500.12860/45006
dc.publisherIEEE
dc.source.beginpage181
dc.source.conference32nd European Signal Processing Conference (EUSIPCO)
dc.source.conferencedate2024-08-26
dc.source.conferencelocationLyon
dc.source.endpage185
dc.source.numberofpages5
dc.title

SRAD-CLF: Squeak and Rattle Anomaly Detection via Contrastive Learning Framework on Real Industrial Noise Recordings

dc.typeProceedings paper
dspace.entity.typePublication
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