An Enhanced Contrastive Ensemble Learning Method for Anomaly Sound Detection

An Enhanced Contrastive Ensemble Learning Method for Anomaly Sound Detection

This paper proposes an enhanced contrastive ensemble learning method for anomaly sound detection. The proposed method achieves approximately 6% in the AUC metric in some categories and achieves state-of-the-art performance among self-supervised models on multiple benchmark datasets. The proposed met...

Szczegółowa specyfikacja

Opis bibliograficzny
Główni autorzy: Jingneng Liao, Fei Yang, Xiaoqing Lu
Format: Artykuł
Język:English
Wydane: MDPI AG 2025-02-01
Seria:Applied Sciences
Hasła przedmiotowe:
anomaly sound detection
deep learning
self-supervised learning
fault diagnosis
Dostęp online:https://www.mdpi.com/2076-3417/15/3/1624

Internet

https://www.mdpi.com/2076-3417/15/3/1624

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