Working Paper Article Version 1 This version is not peer-reviewed

Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Using Classification-Based Methods

Version 1 : Received: 14 August 2021 / Approved: 17 August 2021 / Online: 17 August 2021 (08:36:44 CEST)

How to cite: 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. Preprints 2021, 2021080353 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. Preprints 2021, 2021080353

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

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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