한국음향학회지 (The Journal of the Acoustical Society of Korea)

  • 제40권4호
  • /
  • Pages.337-346
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  • 2021
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  • 1225-4428(pISSN)
  • /
  • 2287-3775(eISSN)
한국음향학회 (The Acoustical Society of Korea)
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병렬 오토인코더 기반의 비정상 신호 탐지

Abnormal signal detection based on parallel autoencoders

  • 이기배 (제주대학교 해양시스템공학과) ;
  • 이종현 (제주대학교 해양시스템공학과)
  • 투고 : 2021.04.20
  • 심사 : 2021.06.07
  • 발행 : 2021.07.31
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초록

일반적으로 비정상 신호 탐지 연구에서는 데이터 불균형으로 인해 정상 신호 특징을 주된 정보로 사용한다. 본 논문에서는 비정상 신호의 특징을 학습하는 병렬 오토인코더를 이용한 효율적인 비정상 신호 탐지기법을 제안한다. 제안된 동일한 구조로 이루어진 병렬 오토인코더는 정상 신호와 비정상 신호에 대한 특징을 학습하는 정상 복원기와 비정상 복원기로 구성되며, 정상 및 비정상 데이터를 순차적으로 학습함으로써 불균형 데이터 문제를 효율적으로 해결할 수 있다. 뿐만 아니라 보다 높은 탐지성능 향상을 위해서 부가적인 이진 분류기가 추가될 수 있다. 공개된 음향데이터를 이용한 실험결과, 제안된 병렬 탐지모델의 학습시간이 단일 오토인코더 탐지모델과 비교하여 약 1.31 ~ 1.61배 늘어나지만, 최소 22 % 이상의 Area Under Curve(AUC) 향상을 보였다. 또한, 사전에 훈련된 병렬 오토인코더를 이용하여 수중 음향데이터를 전이학습한 결과 수중 비정상 신호 AUC 탐지성능을 93 % 이상 향상시킬 수 있음을 확인하였다.

Detection of abnormal signal generally can be done by using features of normal signals as main information because of data imbalance. This paper propose an efficient method for abnormal signal detection using parallel AutoEncoder (AE) which can use features of abnormal signals as well. The proposed Parallel AE (PAE) is composed of a normal and an abnormal reconstructors having identical AE structure and train features of normal and abnormal signals, respectively. The PAE can effectively solve the imbalanced data problem by sequentially training normal and abnormal data. For further detection performance improvement, additional binary classifier can be added to the PAE. Through experiments using public acoustic data, we obtain that the proposed PAE shows Area Under Curve (AUC) improvement of minimum 22 % at the expenses of training time increased by 1.31 ~ 1.61 times to the single AE. Furthermore, the PAE shows 93 % AUC improvement in detecting abnormal underwater acoustic signal when pre-trained PAE is transferred to train open underwater acoustic data.

키워드

과제정보

이 논문은 2020학년도 제주대학교 교원성과지원사업에 의하여 연구되었음.

참고문헌

  1. D. Y. Oh and I. D. Yun, "Residual error based on anomaly detection using auto-encoder in SMD machine sound," Sensors. 18, 1308 (2018). https://doi.org/10.3390/s18051308
  2. K. Suefusa, T. Nishida, H. Purohit, R. Tanabe, T. endo, and Y. Kawaguchi, "Anomalous sound detection based on interpolation deep neural network," Proc. IEEE ICASP. 271-275 (2020).
  3. R. Lang, R. Lu, C. Zhao, H. Qin, and G. Liu, "Graphbased semi-supervised one class support vector machine for detecting abnormal lung sounds," Applied Mathematics and Computation, 364, 124487 (2020). https://doi.org/10.1016/j.amc.2019.06.001
  4. R. Banerjee and A. Ghose, "A semi-supervised approach for identifying abnromal heart sounds using variational autoencoder," Proc. IEEE ICASP. 1249-1253 (2020).
  5. G. Pang, C. Shen, L. Cao, and A. V. D. Hengel, "Deep learning for anomaly detection: A review," ACM Computing Surveys (CSUR), 54, 1-38 (2021).
  6. L. Ruff, R. A. Vandermeulen, L. Deecke, S. A. Siddiqui, A. Binder, E. Muller, and M. Kloft, "Deep one-class classification," Proc. Int. Conf. on machin learning (PMLR), 4393-4402 (2018).
  7. S. Pidhorskyi, R. Almohsen, D. A. Adjeroh, and G. Doretto, "Generative probabilistic novelty detection with adversarial autoencoders," Advances in Neural Information Processing Systems, 31, 6823-6834 (2018).
  8. Y. Koizumi, 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 Lang. Process. 27, 212-224 (2018).
  9. H. Purohit, R. Tanabe, K. Ichige, T. Endo, Y. Nikaido, K. Suefusa, and Y. Kawaguchi, "MIMII dataset: sound dataset for malfunctioning industrial machine investigation and inspection," Detection and Classification of Acoustic Scenes and Events, 209-213 (2019).
  10. Y. Koizumi, Y. Kawaguchi, K. Imoto, T. Nakamura, Y. Nikaido, R. Tanabe, H. Purohit, K. Suefusa, T. Endo, M. Yasuda, and N. Harada, "Description and discussion on DCASE2020 challenge task2: unsupervised anomalous sound detection for machine condition monitoring," Detection and Classification of Acoustic Scenes and Events, 81-85 (2020).
  11. H. Yang, S. Byun, K. Lee, Y. Choo, and K. Kim, "Underwater acoustic research trends with machine learning: General background," J. Ocean Eng. Technol. 34, 147-154 (2020). https://doi.org/10.26748/KSOE.2020.015
  12. H. Yang, K. Lee, Y. Choo, and K. Kim, "Underwater acoustic research trends with machine learning: Passive SONAR applications," J. Ocean Eng. Technol. 34, 227-236 (2020). https://doi.org/10.26748/KSOE.2020.017
  13. H. Yang, S. Byun, K. Lee, and K. Kim, "Underwater acoustic research trends with machine learning: Active SONAR applications," J. Ocean Eng. Technol. 34, 277-284 (2020). https://doi.org/10.26748/KSOE.2020.018
  14. R. J. Urick, Principles of Uunderwater Sound (Peninsula, Westport, 1993), pp. 181-200.
  15. D. S. Dominguez, S. T. Guijarro, A. C. Lopez, and A. P. Gimenez, "ShipEar: An underwater vessel noise database," Applied Acoustics, 113, 64-69 (2016). https://doi.org/10.1016/j.apacoust.2016.06.008
  16. K. J. V. Raposa, G. Scowcroft, J. H. Miller, D. R. Ketten, and A. N. Popper, "Discovery of sound in the sea: Resources for educators, students, the public, and policymakers," in Handbook of The Effects of Noise on Aquatic Life 2, edited by A. N. Popper and A. Hawkins (Springer, New York, 2016).