Transformer-based autoencoder with ID constraint for unsupervised anomalous sound detection

Transformer-based autoencoder with ID constraint for unsupervised anomalous sound detection

Abstract Unsupervised anomalous sound detection (ASD) aims to detect unknown anomalous sounds of devices when only normal sound data is available. The autoencoder (AE) and self-supervised learning based methods are two mainstream methods. However, the AE-based methods could be limited as the feature...

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Bibliographic Details
Main Authors: Jian Guan, Youde Liu, Qiuqiang Kong, Feiyang Xiao, Qiaoxi Zhu, Jiantong Tian, Wenwu Wang
Format: Article
Language:English
Published: SpringerOpen 2023-10-01
Series:EURASIP Journal on Audio, Speech, and Music Processing
Subjects:
Anomalous sound detection
Autoencoder
ID classifier
Weighted anomaly score computation
Online Access:https://doi.org/10.1186/s13636-023-00308-4

Internet

https://doi.org/10.1186/s13636-023-00308-4

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