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Distributed collaborative anomalous sound detection by embedding sharing

arXiv 2403.16610 ↗First posted 25 Mar 2024eess.AS cs.CR cs.LGcited by 0

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Abstract (arXiv)

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%.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 25 references that are in the map. First posted on arXiv 25 Mar 2024.

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References · 12

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  3. 2018Federated Learning with Non-IID Datacited by 1,099
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  5. 2017Look, Listen and Learncited by 452
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  7. 2020Information Leakage in Embedding Modelscited by 199
  8. 2018MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devicescited by 180
  9. 2020End-to-End Evaluation of Federated Learning and Split Learning for Internet of Thingscited by 64
  10. 2020Description and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoringcited by 43
  11. 2020Anomalous sound detection based on interpolation deep neural networkcited by 33
  12. 2021Flow-based Self-supervised Density Estimation for Anomalous Sound Detectioncited by 15

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