All Issue

2023 Vol.42, Issue 6 Preview Page

Research Article

30 November 2023. pp. 500-510
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
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
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
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
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
B. Schölkopf, R. Willianson, A. Smola, J. S. Taylor, and J. Platt, "Support vector method for novelty detection," Proc. NIPS. 582-588 (1999).
D. M. Tax and R. P. Duin, "Support vector data description," Machine learning, 59, 45-66 (2004). 10.1023/B:MACH.0000008084.60811.49
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
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).
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).
M. Sakurada and T. Yairi, "Anomaly detection using autoencoders with nonlinear dimensionality reduction," Proc. MLSDA, 4-11 (2014). 10.1145/2689746.2689747
K. Lee and C. H. Lee, "Abnormal signal detection based on parallel autoencoders," J. Acoust. Soc. Kr. 40, 337-346 (2021).
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
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
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
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
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
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).
A. Oord, O. Vinyals, and K. Kavukcuoglu, "Neural discrete representation learning," Proc. NIPS, 1-17 (2017).
W. Williams, S. Ringer, T. Ash, J. Hughes, D. MacLeod, and J. Dougherty, "Hierarchical quantized autoencoders," Proc. NIPS, 1-12 (2020).
A New Mindset for the Army: Silent Running,, (Last viewed September 1, 2023).
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).
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).
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
L. E. Kinsler, A. R. Frey, A. B. Coppens, and J. V. Sanders, Fundamentals of Acoustics (John Wiley & Sons, New Jersey, 1999), Chap. 15.
D. P. Kingma and M. Welling, "An introduction to variational autoencoders," Found. Trends Mach. Learn. 12, 1-85 (2019). 10.1561/9781680836233
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
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).
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).
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
  • 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