Transformer and Graph Convolution-Based Unsupervised Detection of Machine Anomalous Sound Under Domain Shifts

Thanks to the development of deep learning, machine abnormal sound detection (MASD) based on unsupervised learning has exhibited excellent performance. However, in the task of unsupervised MASD, there are discrepancies between the acoustic characteristics of the test set and the training set under t...

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Veröffentlicht in:	IEEE transactions on emerging topics in computational intelligence Jg. 8; H. 4; S. 2827 - 2842
Hauptverfasser:	Yan, Jingke, Cheng, Yao, Wang, Qin, Liu, Lei, Zhang, Weihua, Jin, Bo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.08.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Artificial neural networks Computational modeling Convolution Data mining Data models Deep learning domain shift Feature extraction machine anomalous sound detection Machine learning Physical properties Sound Task analysis Time-frequency analysis Transformers Unsupervised learning
ISSN:	2471-285X, 2471-285X
Online-Zugang:	Volltext
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