Abstract
Automatic detection of anomalous sounds is very important for industrial equipment maintenances. However, anomalous sounds are difficult to collect in practice, and self-supervised methods have received extensive attentions. It is well-known that the self-supervised methods show poor performances on certain machine types. To improve the detection performance, in this work, we introduce other types of data as targets to train a general classifier. After that, the model has certain prior knowledge, and then we fine tune the parameters of the model for a specific machine type. We also studied the impact of input features on performance, and it is shown that for machine types, filtering out low-frequency noise interference can significantly improve model performance. Experiments conducted using the DCASE 2021 Challenge Task2 dataset showed that the proposed method improves the detection performance on each machine type and outperforms the DCASE 2021 Challenge first-placed ensemble model by \(8.73\%\) on average according to the official scoring method.
This work is supported by the Natural Science Foundation of Chongqing, China (No. cstc2021jcyj-bshX0206).
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Zeng, Y., Liu, H., Zhao, Y., Zhou, Y. (2023). Self-supervised Anomalous Sound Detection for Machine Condition Monitoring. In: Gao, F., Wu, J., Li, Y., Gao, H. (eds) Communications and Networking. ChinaCom 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 500. Springer, Cham. https://doi.org/10.1007/978-3-031-34790-0_17
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