Distributed collaborative anomalous sound detection by embedding sharing
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866909149358456832 |
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| author | Dohi, Kota Kawaguchi, Yohei |
| author_facet | Dohi, Kota Kawaguchi, Yohei |
| contents | To develop a machine sound monitoring system, a method for detecting anomalous sound is proposed. In this paper, we explore a method for multiple clients to collaboratively learn an anomalous sound detection model while keeping their raw data private from each other. In the context of industrial machine anomalous sound detection, each client possesses data from different machines or different operational states, making it challenging to learn through federated learning or split learning. In our proposed method, each client calculates embeddings using a common pre-trained model developed for sound data classification, and these calculated embeddings are aggregated on the server to perform anomalous sound detection through outlier exposure. Experiments showed that our proposed method improves the AUC of anomalous sound detection by an average of 6.8%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_16610 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Distributed collaborative anomalous sound detection by embedding sharing Dohi, Kota Kawaguchi, Yohei Audio and Speech Processing Cryptography and Security Machine Learning Sound To develop a machine sound monitoring system, a method for detecting anomalous sound is proposed. In this paper, we explore a method for multiple clients to collaboratively learn an anomalous sound detection model while keeping their raw data private from each other. In the context of industrial machine anomalous sound detection, each client possesses data from different machines or different operational states, making it challenging to learn through federated learning or split learning. In our proposed method, each client calculates embeddings using a common pre-trained model developed for sound data classification, and these calculated embeddings are aggregated on the server to perform anomalous sound detection through outlier exposure. Experiments showed that our proposed method improves the AUC of anomalous sound detection by an average of 6.8%. |
| title | Distributed collaborative anomalous sound detection by embedding sharing |
| topic | Audio and Speech Processing Cryptography and Security Machine Learning Sound |
| url | https://arxiv.org/abs/2403.16610 |