Deep Convolutional Variational Autoencoder for Anomalous Sound Detection

Anomalous sound detection (ASD) is one of the most important fields in industrial facility maintenance. For this task, semi-supervised approaches are preferred thanks to their simplicity and no training data labels required. These methods train an autoencoder (AE) with only normal sound data and det...

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Veröffentlicht in:2020 IEEE Eighth International Conference on Communications and Electronics (ICCE) S. 313 - 318
Hauptverfasser: Nguyen, Minh-Hieu, Nguyen, Duy-Quang, Nguyen, Dinh-Quoc, Pham, Cong-Nguyen, Bui, Dai, Han, Huy-Dung
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 13.01.2021
Schlagworte:
anomalous sound detection
autoencoder
Conferences
Convolution
Industrial facilities
machine sound monitoring
Maintenance engineering
Monitoring
semi-supervised learning
Task analysis
Training data
ISBN:9781728154695, 1728154693
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:Anomalous sound detection (ASD) is one of the most important fields in industrial facility maintenance. For this task, semi-supervised approaches are preferred thanks to their simplicity and no training data labels required. These methods train an autoencoder (AE) with only normal sound data and detect anomalies based on anomaly scores of actual samples. In this paper, we propose applying the convolutional variational autoencoder (CVAE) to ASD task. Through experiments using machine sound data, the CVAE is proven to be effective in detecting abnormal sound and outperform existing methods.
ISBN:9781728154695
1728154693
DOI:10.1109/ICCE48956.2021.9352085