Anomalous Sound Detection Using Spectral-Temporal Information Fusion

Liu, Y; Guan, J; Zhu, Q; Wang, W

Anomalous Sound Detection Using Spectral-Temporal Information Fusion

Liu, Y Guan, J Zhu, Q

Wang, W

Permalink

Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, 2022-May, pp. 816-820
Issue Date:: 2022-04-27

Embargoed

	Filename	Description	Size
	Anomalous Sound Detection.pdf	Accepted version	917.8 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Embargoed
Open Access

This item is currently unavailable due to the publisher's embargo.

The embargo period expires on 27 Apr 2024

Full metadata record

Field	Value	Language
dc.contributor.author	Liu, Y
dc.contributor.author	Guan, J
dc.contributor.author	Zhu, Q https://orcid.org/0000-0003-3942-0945
dc.contributor.author	Wang, W
dc.date	2022-05-23
dc.date.accessioned	2023-01-18T06:02:48Z
dc.date.available	2023-01-18T06:02:48Z
dc.date.issued	2022-04-27
dc.identifier.citation	ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, 2022-May, pp. 816-820
dc.identifier.isbn	9781665405409
dc.identifier.issn	1520-6149
dc.identifier.uri	http://hdl.handle.net/10453/165148
dc.description.abstract	Unsupervised anomalous sound detection aims to detect unknown abnormal sounds of machines from normal sounds. However, the state-of-the-art approaches are not always stable and perform dramatically differently even for machines of the same type, making it impractical for general applications. This paper proposes a spectral-temporal fusion based self-supervised method to model the feature of the normal sound, which improves the stability and performance consistency in detection of anomalous sounds from individual machines, even of the same type. Experiments on the DCASE 2020 Challenge Task 2 dataset show that the proposed method achieved 81.39%, 83.48%, 98.22% and 98.83% in terms of the minimum AUC (worst-case detection performance amongst individuals) in four types of real machines (fan, pump, slider and valve), respectively, giving 31.79%, 17.78%, 10.42% and 21.13% improvement compared to the state-of-the-art method, i.e., Glow_Aff. Moreover, the proposed method has improved AUC (average performance of individuals) for all the types of machines in the dataset. The source codes are available at https://github.com/liuyoude/STgram_MFN
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
dc.relation.ispartof	ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing
dc.relation.isbasedon	10.1109/icassp43922.2022.9747868
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Anomalous Sound Detection Using Spectral-Temporal Information Fusion
dc.type	Conference Proceeding
utslib.citation.volume	2022-May
utslib.location.activity	Singapore, Singapore
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	embargoed	*
utslib.copyright.embargo	2024-04-27T00:00:00+1000Z
dc.date.updated	2023-01-18T06:02:46Z
pubs.finish-date	2022-05-27
pubs.publication-status	Published
pubs.start-date	2022-05-23
pubs.volume	2022-May

Abstract:

Unsupervised anomalous sound detection aims to detect unknown abnormal sounds of machines from normal sounds. However, the state-of-the-art approaches are not always stable and perform dramatically differently even for machines of the same type, making it impractical for general applications. This paper proposes a spectral-temporal fusion based self-supervised method to model the feature of the normal sound, which improves the stability and performance consistency in detection of anomalous sounds from individual machines, even of the same type. Experiments on the DCASE 2020 Challenge Task 2 dataset show that the proposed method achieved 81.39%, 83.48%, 98.22% and 98.83% in terms of the minimum AUC (worst-case detection performance amongst individuals) in four types of real machines (fan, pump, slider and valve), respectively, giving 31.79%, 17.78%, 10.42% and 21.13% improvement compared to the state-of-the-art method, i.e., Glow_Aff. Moreover, the proposed method has improved AUC (average performance of individuals) for all the types of machines in the dataset. The source codes are available at https://github.com/liuyoude/STgram_MFN

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/165148