All Issue

2023 Vol.42, Issue 6 Preview Page

Research Article

Abnormal sonar signal detection using recurrent neural network and vector quantization

순환신경망과 벡터 양자화를 이용한 비정상 소나 신호 탐지

Kibae Lee1
,
Guhn Hyeok Ko1
,
Chong Hyun Lee1*
이 기배1
,
고 건혁1
,
이 종현1*
1제주대학교 해양시스템공학과
*Corresponding Author
30 November 2023. pp. 500-510
PDF XML Citation
Abstract

Passive sonar signals mainly contain both normal and abnormal signals. The abnormal signals mixed with normal signals are primarily detected using an AutoEncoder (AE) that learns only normal signals. However, existing AEs may perform inaccurate detection by reconstructing distorted normal signals from mixed signal. To address these limitations, we propose an abnormal signal detection model based on a Recurrent Neural Network (RNN) and vector quantization. The proposed model generates a codebook representing the learned latent vectors and detects abnormal signals more accurately through the proposed search process of code vectors. In experiments using publicly available underwater acoustic data, the AE and Variational AutoEncoder (VAE) using the proposed method showed at least a 2.4 % improvement in the detection performance and at least a 9.2 % improvement in the extraction performance for abnormal signals than the existing models.

Keywords
Abnormal signal detection
Autoencoder
Vector quantization
Recurrent neural network

수동소나 신호에는 정상신호와 비정상 신호가 같이 존재하는 경우가 대부분이다. 정상신호와 혼재된 비정상 신호는 주로 정상신호만을 학습하는 오토인코더를 이용하여 탐지된다. 하지만 기존의 오토인코더는 혼재된 신호로부터 왜곡된 정상신호를 복원하므로 부정확한 탐지를 수행할 수 있다. 이러한 한계를 개선하고자, 본 논문에서는 순환신경망과 벡터 양자화 기반의 비정상 신호 탐지 모델을 제안한다. 제안된 모델은 학습된 잠재벡터들을 대표하는 코드 북을 생성하고, 제안된 코드벡터의 탐색을 통해 보다 정확하게 비정상 신호를 탐지한다. 공개된 수중 음향 데이터를 이용한 실험에서 제안된 기법이 적용된 오토인코더와 변이형 오토인코더는 기존 모델에 비해 최소 2.4 % 향상된 탐지 성능과 최소 9.2 % 높은 비정상 신호 추출 성능을 보였다.

키워드
비정상 신호 탐지
오토인코더
벡터 양자화
순환신경망
References
1
L. Ruff, J. R. Kauffmann, R. A. Vandermeulen, G. Monatavon, W. Samek, M. Kloft, T. G. Dietterich, and K. R. Müller, "A unifying review of deep and shallow anomaly detection," Proc. IEEE, 109, 756-795 (2021). 10.1109/JPROC.2021.3052449
2
K. Lee, C. H. Lee, and J. Lee, "Semi-supervised anomaly detection algorithm using probabilistic labeling (SAD-PL)," IEEE Access, 9, 142972-142981 (2021). 10.1109/ACCESS.2021.3120710
3
K. Lee, G. H. Ko, and C. H. Lee, "Encoding and decoding-based normalizing flows for image anomaly localization," Electron. Lett. 59, e212829 (2023). 10.1049/ell2.12829
4
W. Hu, J. Gao, B. Li, O. Wu, J. Du, and S. Maybank, "Anomaly detection using local kernel density estimation and context-based regression," IEEE Trans. Knowl. Data Eng. 32, 218-233 (2020). 10.1109/TKDE.2018.2882404
5
L. Huang, X. Nguyen, M. Garofalakis, M. I. Jordan, A. Joseph, and N. Taft, "In-network PCA and anomaly detection," Proc. NIPS, 617-624 (2006). 10.7551/mitpress/7503.003.0082
6
B. Schölkopf, R. Willianson, A. Smola, J. S. Taylor, and J. Platt, "Support vector method for novelty detection," Proc. NIPS. 582-588 (1999).
7
D. M. Tax and R. P. Duin, "Support vector data description," Machine learning, 59, 45-66 (2004). 10.1023/B:MACH.0000008084.60811.49
8
C. H. Lee and K. Lee, "Semi-supervised anomaly detection algorithm based on KL divergence (SAD-KL)," Proc. ADIX, 118-123 (2023). 10.1117/12.2661178
9
L. Ruff, R. A. Vandermeulen, N. Görnits, L. Deecke, S. A. Siddiqui, A. Binder, E. Müller, and M. Kloft, "Deep one-class classification," Proc. ICML, 4393-4402 (2018).
10
K. Lee, G. H. Ko, and C. H. Lee, "Online anomaly detection algorithm based on deep support vector data description using incremental centroid update," J. Acoust. Soc. Kr. 41, 199-209 (2022).
11
M. Sakurada and T. Yairi, "Anomaly detection using autoencoders with nonlinear dimensionality reduction," Proc. MLSDA, 4-11 (2014). 10.1145/2689746.2689747
12
K. Lee and C. H. Lee, "Abnormal signal detection based on parallel autoencoders," J. Acoust. Soc. Kr. 40, 337-346 (2021).
13
Z. Mnasri, S. Rovetta, and F. Masulli, "Anomalous sound event detection: A survey of machine learning based methods and applications," Multimed. Tools App. 81, 5537-5586 (2022). 10.1007/s11042-021-11817-9
14
E. Marchi, F. Vesperini, S. Squartini, and B. Schuller, "Deep recurrent neural networks-based autoencoders for acoustic novelty detection," Comput. Intell. Neurosci. 2017, 1-14 (2017). 10.1155/2017/469486028182121PMC5274684
15
K. Suefusa, T. Nishida, H. Purohit, R. Tanabe, T. Endo, and Y. Kawaguchi, "Anomalous sound detection based on interpolation deep neural network," Proc. ICASSP, 271-275 (2020). 10.1109/ICASSP40776.2020.9054344
16
Y. Tagawa, R. Maskeliunas, and R. Damasevicius, "Acoustic anomaly detection of mechanical failures in noisy real-life factory environments," Electronics, 10, 2329 (2021). 10.3390/electronics10192329
17
P. J. Pereira, G. Coelho, A. Ribeiro, L. M. Matos, E. C. Nunes, A. Ferreira, A. Pilastri, and P. Cortes, "Using deep autoencoders for in-vehicle audio anomaly detection," Procedia. Comput. Sci. 192, 298-307 (2021). 10.1016/j.procs.2021.08.031
18
D. Dehaene, O. Frigo, S. Combrexelle, and P. Eline, "Iterative energy-based projection on a normal data manifold for anomaly localization," Proc. ICLR, 1-17 (2020).
19
A. Oord, O. Vinyals, and K. Kavukcuoglu, "Neural discrete representation learning," Proc. NIPS, 1-17 (2017).
20
W. Williams, S. Ringer, T. Ash, J. Hughes, D. MacLeod, and J. Dougherty, "Hierarchical quantized autoencoders," Proc. NIPS, 1-12 (2020).
21
A New Mindset for the Army: Silent Running, https://www.c4isrnet.com/opinion/2019/12/06/a-new-mindset-for-the-army-silent-running/, (Last viewed September 1, 2023).
22
S. Kim, S. K. Jung, D. Kang, M. Kim, and S. Cho, "Application of the artificial intelligence for automatic detection of shipping noise in shallow-water" (in Korean), J. Acoust. Soc. Kr. 39, 279-285 (2020).
23
K. M. Park and D. Kim, "Preprocessing performance of convolutional neural networks according to characteristic of underwater targets" (in Korean), J. Acoust. Soc. Kr. 41, 629-636 (2022).
24
Y. C. Jung, B. U. Kim, S. K. An, W. J. Seong, K. H. Lee, and J. Y. Hahn, "An algorithm for submarine passive sonar simulator" (in Korean), J. Acoust. Soc. Kr. 32, 472-483 (2013). 10.7776/ASK.2013.32.6.472
25
L. E. Kinsler, A. R. Frey, A. B. Coppens, and J. V. Sanders, Fundamentals of Acoustics (John Wiley & Sons, New Jersey, 1999), Chap. 15.
26
D. P. Kingma and M. Welling, "An introduction to variational autoencoders," Found. Trends Mach. Learn. 12, 1-85 (2019). 10.1561/9781680836233
27
D. S. Dominguez, S. T. Guijarro, A. C. Lopez, and A. P. Gimenez, "ShipEar: An underwater vessel noise database," Appl. Acoust. 113, 64-69 (2016). 10.1016/j.apacoust.2016.06.008
28
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).
29
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," Proc. DCASE, 81-85 (2020).
30
H. Rezatofighi, N. Tsoi, J. Y. Gwak, A. Sadeghian, I. Reid, and S. Savarese, "Generalized intersection over union: A metric and a loss for bounding box regression," Proc. CVPR, 658-666 (2019). 10.1109/CVPR.2019.00075
Information
  • Publisher :The Acoustical Society of Korea
  • Publisher(Ko) :한국음향학회
  • Journal Title :The Journal of the Acoustical Society of Korea
  • Journal Title(Ko) :한국음향학회지
  • Volume : 42
  • No :6
  • Pages :500-510
  • Received Date : 2023-07-03
  • Revised Date : 2023-09-14
  • Accepted Date : 2023-10-16
  • DOI :https://doi.org/10.7776/ASK.2023.42.6.500
Journal Informaiton The Journal of the Acoustical Society of Korea The Journal of the Acoustical Society of Korea
E-Book
  • scopus_JASK
  • ESCI_JASK
  • Google scholar_JASK
  • NRF
  • KOFST
  • KISTI Current Status
  • KISTI Cited-by
  • crosscheck
  • orcid
  • ccl
Journal Informaiton Journal Informaiton - close