Poster Abstract: Adapting Pretrained Features for Efficient Unsupervised Acoustic Anomaly Detection

dc.contributor.author	Liu, Zhaoyi
dc.contributor.author	Michiels, Sam
dc.contributor.author	Joosen, Wouter
dc.contributor.author	Hughes, Danny
dc.contributor.imecauthor	Joosen, Wouter
dc.date.accessioned	2022-11-15T10:47:38Z
dc.date.available	2022-09-29T02:51:21Z
dc.date.available	2022-11-15T10:47:38Z
dc.date.issued	2022
dc.description.wosFundingText	This research is partially funded by the Research Fund KU Leuven, and by imec and Flanders Innovation Entrepreneurship (VLAIO) in the context of the WiPeR project.
dc.identifier.doi	10.1109/IPSN54338.2022.00063
dc.identifier.eisbn	978-1-6654-9624-7
dc.identifier.issn	na
dc.identifier.uri	https://imec-publications.be/handle/20.500.12860/40520
dc.publisher	IEEE COMPUTER SOC
dc.source.beginpage	525
dc.source.conference	21st ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)
dc.source.conferencedate	MAY 04-06, 2022
dc.source.conferencelocation	Milano, Italy
dc.source.endpage	526
dc.source.journal	na
dc.source.numberofpages	2
dc.title	Poster Abstract: Adapting Pretrained Features for Efficient Unsupervised Acoustic Anomaly Detection
dc.type	Proceedings paper
dspace.entity.type	Publication
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