• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

CutPaste-Based Anomaly Detection Model using Multi Scale Feature Extraction in Time Series Streaming Data

Vol. 16, No. 8, August 31, 2022
10.3837/tiis.2022.08.018, Download Paper (Free):

Abstract

The aging society increases emergency situations of the elderly living alone and a variety of social crimes. In order to prevent them, techniques to detect emergency situations through voice are actively researched. This study proposes CutPaste-based anomaly detection model using multi-scale feature extraction in time series streaming data. In the proposed method, an audio file is converted into a spectrogram. In this way, it is possible to use an algorithm for image data, such as CNN. After that, mutli-scale feature extraction is applied. Three images drawn from Adaptive Pooling layer that has different-sized kernels are merged. In consideration of various types of anomaly, including point anomaly, contextual anomaly, and collective anomaly, the limitations of a conventional anomaly model are improved. Finally, CutPaste-based anomaly detection is conducted. Since the model is trained through self-supervised learning, it is possible to detect a diversity of emergency situations as anomaly without labeling. Therefore, the proposed model overcomes the limitations of a conventional model that classifies only labelled emergency situations. Also, the proposed model is evaluated to have better performance than a conventional anomaly detection model.


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Cite this article

[IEEE Style]
B. Jeon and K. Chung, "CutPaste-Based Anomaly Detection Model using Multi Scale Feature Extraction in Time Series Streaming Data," KSII Transactions on Internet and Information Systems, vol. 16, no. 8, pp. 2787-2800, 2022. DOI: 10.3837/tiis.2022.08.018.

[ACM Style]
Byeong-Uk Jeon and Kyungyong Chung. 2022. CutPaste-Based Anomaly Detection Model using Multi Scale Feature Extraction in Time Series Streaming Data. KSII Transactions on Internet and Information Systems, 16, 8, (2022), 2787-2800. DOI: 10.3837/tiis.2022.08.018.

[BibTeX Style]
@article{tiis:25920, title="CutPaste-Based Anomaly Detection Model using Multi Scale Feature Extraction in Time Series Streaming Data", author="Byeong-Uk Jeon and Kyungyong Chung and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2022.08.018}, volume={16}, number={8}, year="2022", month={August}, pages={2787-2800}}