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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References
Koizumi, Y., Saito, S., Uematsu, H., Harada, N.: Optimizing acoustic feature extractor for anomalous sound detection based on Neyman-Pearson lemma. In: 25th European Signal Processing Conference (EUSIPCO), pp. 698–702. IEEE (2017)
Koizumi, Y., Saito, S., Uematsu, H., Kawachi, Y., Harada, N.: Unsupervised detection of anomalous sound based on deep learning and the Neyman-Pearson lemma. IEEE/ACM Trans. Audio Speech Lang. Process. 27(1), 212–224 (2018)
Koizumi, Y., et al.: Description and discussion on DCASE 2020 challenge task2: unsupervised anomalous sound detection for machine condition monitoring. arXiv preprint arXiv:2006.05822 (2020)
Kawaguchi, Y., et al.: Description and discussion on DCASE 2021 challenge task 2: unsupervised anomalous sound detection for machine condition monitoring under domain shifted conditions. arXiv preprint arXiv:2106.04492 (2021)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. (CSUR) 41(3), 1–58 (2009)
Dohi, K., Endo, T., Purohit, H., Tanabe, R., Kawaguchi, Y.: Flow-based self-supervised density estimation for anomalous sound detection. In: 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2021, pp. 336–340. IEEE (2021)
Lopez, J., Stemmer, G., Lopez-Meyer, P., Singh, P.S., del Hoyo Ontiveros, J.A., Courdourier, H.A.: Ensemble of complementary anomaly detectors under domain shifted conditions. DCASE2021 Challenge, Technical report (2021)
Morita, K., Yano, T., Tran, K.: Anomalous sound detection using CNN-based features by self supervised learning. DCASE2021 Challenge, Technical report (2021)
Wilkinghoff, K.: Utilizing sub-cluster adacos for anomalous sound detection under domain shifted conditions. DCASE2021 Challenge, Technical report (2021)
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C.: MobileNetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)
Chen, S., Liu, Y., Gao, X., Han, Z.: MobileFaceNets: efficient CNNs for accurate real-time face verification on mobile devices. In: Zhou, J., et al. (eds.) CCBR 2018. LNCS, vol. 10996, pp. 428–438. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-97909-0_46
Wen, Y., Zhang, K., Li, Z., Qiao, Y.: A comprehensive study on center loss for deep face recognition. Int. J. Comput. Vis. 127(6), 668–683 (2019)
De Maesschalck, R., Jouan-Rimbaud, D., Massart, D.L.: The mahalanobis distance. Chemom. Intell. Lab. Syst. 50(1), 1–18 (2000)
Xiang, S., Nie, F., Zhang, C.: Learning a mahalanobis distance metric for data clustering and classification. Pattern Recogn. 41(12), 3600–3612 (2008)
Thulasidasan, S., Chennupati, G., Bilmes, J., Bhattacharya, T., Michalak, S.: On mixup training: improved calibration and predictive uncertainty for deep neural networks. arXiv preprint arXiv:1905.11001 (2019)
Harada, N., Niizumi, D., Takeuchi, D., Ohishi, Y., Yasuda, M., Saito, S.: ToyADMOS2: another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions. arXiv preprint arXiv:2106.02369 (2021)
Tanabe, R.: MIMII due: sound dataset for malfunctioning industrial machine investigation and inspection with domain shifts due to changes in operational and environmental conditions. arXiv preprint arXiv:2105.02702 (2021)
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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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