Anomalous sound detection method based on STgram-MFN optimization

Shasha Cheng; Hao Shen; Dexin Zhao; Qing Liu; Guanjun Jing; Lei Wang; Jie Yang

doi:10.1117/12.2685358

21 July 2023 Anomalous sound detection method based on STgram-MFN optimization

Shasha Cheng, Hao Shen, Dexin Zhao, Qing Liu, Guanjun Jing, Lei Wang, Jie Yang

Proceedings Volume 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023); 1271707 (2023) https://doi.org/10.1117/12.2685358
Event: 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 2023, Wuhan, China

Abstract

Aiming at solving the problems of insufficient feature information extraction and low accuracy in conventional anomalous sound detection methods, this paper presents a new method for detecting anomalous sound based on STgram-MFN optimization. By fusing multiple attention mechanisms for feature recalibration, it can selectively emphasize features with high informative content and suppress less useful features, thereby improving the accuracy of anomalous sound detection. Experiments on the DCASE 2020 Challenge Task two dataset show that compared with the original STgram-MFN, Its AUC has reached 94.20%, 74.29%, 88.82%, 92.86%, 99.29%, 98.06% (ToyCar, Toycar, Fan, Pump, Slider, Valve). Respectively, increased by 1.56%, 1.37%, 4.05%, 2.87%, 0.04% and 2.91%. In addition, the average AUC of our proposed method is improved by 2.13%.

Citation Download Citation

Shasha Cheng, Hao Shen, Dexin Zhao, Qing Liu, Guanjun Jing, Lei Wang, and Jie Yang "Anomalous sound detection method based on STgram-MFN optimization", Proc. SPIE 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 1271707 (21 July 2023); https://doi.org/10.1117/12.2685358

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