Improving Anomalous Sound Detection by Distance Matrix-Based Visualization of Measurement Flaws
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Published
Sep 4, 2023
Nobuaki Tanaka
Takeru Shiraga
Yusuke Itani
Abstract
Although recent DNN-based methods have improved the performance of anomalous sound detection systems, it is still difficult to deploy a system in a real environment without performance degradation. This is often due to measurement flaws such as sensor variability, poor setup, or environmental noise. Since such adverse effects are difficult to model by machine learning, a practical approach to this issue is for humans to identify such flaws and correct them. To this end, we propose a method to visualize measurement flaws as a heatmap based on the distance matrix of the samples in the dataset. This method is designed to find unexpected flaws in the measurement process. Using this method, we were able to identify measurement flaws of anomalous sound detection systems in real production lines. The robustness of anomalous sound detection can be improved by correcting the flaws found by our method.
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Keywords
anomalous sound detection, product inspection, machine health monitoring, data visualization
References
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Y. Koizumi, S. Saito, H. Uematsu, and N. Harada, “Optimizing acoustic feature extractor for anomalous sound detection based on neyman-pearson lemma,” in Proc. EUSIPCO, 2017, pp. 698–702.
Y. Koizumi, S. Saito, H. Uematsu, Y. Kawachi, and N. Harada, “Unsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson Lemma,” IEEE/ACM Trans. on Audio Speech and Language Processing, pp.212–224, 2019.
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G. Wichern, A. Chakrabarty, Z.-Q. Wang, and J. Le Roux, “Anomalous sound detection using attentive neural processes,” in Proc. WASPAA, 2021, pp. 186–190.
N. Harada, D. Niizumi, D. Takeuchi, Y. Ohishi, M. Yasuda, and S. Saito, “ToyADMOS2: another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions,” arXiv preprint arXiv:2106.02369, 2021.
K. Dohi, T. Nishida, H. Purohit, R. Tanabe, T. Endo, M. Yamamoto, Y. Nikaido, and Y. Kawaguchi, “Mimii dg: Sound dataset for malfunctioning industrial machine investigation and inspection for domain generalization task,” arXiv preprint arXiv:2205.13879, 2022.
S. Venkatesh, G. Wichern, A. Subramanian, and J. Le Roux, “Disentangled surrogate task learning for improved domain generalization in unsupervised anomalous sound detection,” DCASE2022 Challenge, Tech. Rep., 2022.
J. Luo et al., “A comparison of batch effect removal methods for enhancement of prediction performance using MAQC-II microarray gene expression data,” Pharmacogenomics J. (2010) 10, pp. 278–291.
C. Lazar et al., “Batch efect removal methods for microarray gene expression data integration: a survey,” Brief. Bioinform. vol. 14, no. 4, pp. 469–490, 2012.
D. J. McCarthy, K. R. Campbell, A. T. K. Lun, and Q. F. Wills, “Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R,” Bioinformatics, 33(8), 2017, pp. 1179–1186.
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J. T. Leek and J. D. Storey, “Capturing heterogeneity in gene expression studies by surrogate variable analysis,” PLoS Genet. 3(9): e161, 2007.
J. T. Leek, W. E. Johnson, H. S. Parker, A. E. Jaffe, and J. D. Storey, “The sva package for removing batch effects and other unwanted variation in high-throughput experiments,” Bioinformatics vol. 28, no. 6, 2012, pp. 882–883.
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J. M. Franks, G. Cai, and M. L. Whitfield, “Feature specific quantile normalization enables cross-platform classification of molecular subtypes using gene expression data,” Bioinformatics, vol. 34, no. 11, 2018, pp. 1868– 1874.
C. H. You, K. A. Lee, and H. Li, “An SVM kernel with GMM-supervector based on the Bhattacharyya distance for speaker recognition,” IEEE Signal Process. Lett. vol. 16, no. 1, pp. 49–52, 2009.
N. T. Vu et al., “A first speech recognition system for Mandarin-English code-switch conversational speech,” in Proc. ICASSP, Mar. 2012, pp. 4889–4892.
Section
Regular Session Papers
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