Wang, Y.; Zheng, Y.; Zhang, Y.; Xie, Y.; Xu, S.; Hu, Y.; He, L. Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Using Classification-Based Methods. Preprints2021, 2021080353
APA Style
Wang, Y., Zheng, Y., Zhang, Y., Xie, Y., Xu, S., Hu, Y., & He, L. (2021). Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Using Classification-Based Methods. Preprints. https://doi.org/
Chicago/Turabian Style
Wang, Y., Ying Hu and Liang He. 2021 "Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Using Classification-Based Methods" Preprints. https://doi.org/
Abstract
The task of unsupervised anomalous sound detection (ASD) is challenging for detecting anomalous sounds from a large audio database without any annotated anomalous training data. Many unsupervised methods were proposed, but previous works have confirmed that the classification-based models far exceeds the unsupervised models in ASD. In this paper, we adopt two classification-based anomaly detection models: (1) Outlier classifier is to distinguish anomalous sounds or outliers from the normal; (2) ID classifier identifies anomalies using both the confidence of classification and the similarity of hidden embeddings. We conduct experiments in task 2 of DCASE 2020 challenge, and our ensemble method achieves an averaged area under the curve (AUC) of 95.82% and averaged partial AUC (pAUC) of 92.32%, which outperforms the state-of-the-art models.
Keywords
Unsupervised anomalous sound detection; classification-based model; Outlier classifier; ID classifier
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
