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http://dx.doi.org/10.26748/KSOE.2020.015

Underwater Acoustic Research Trends with Machine Learning: General Background  

Yang, Haesang (Department of Naval Architecture & Ocean Engineering, Seoul National University)
Lee, Keunhwa (Department of Defense System Engineering, Sejong University)
Choo, Youngmin (Department of Defense System Engineering, Sejong University)
Kim, Kookhyun (School of Naval Architecture & Ocean Engineering, Tongmyong University)
Publication Information
Journal of Ocean Engineering and Technology / v.34, no.2, 2020 , pp. 147-154 More about this Journal
Abstract
Underwater acoustics that is the study of the phenomenon of underwater wave propagation and its interaction with boundaries, has mainly been applied to the fields of underwater communication, target detection, marine resources, marine environment, and underwater sound sources. Based on the scientific and engineering understanding of acoustic signals/data, recent studies combining traditional and data-driven machine learning methods have shown continuous progress. Machine learning, represented by deep learning, has shown unprecedented success in a variety of fields, owing to big data, graphical processor unit computing, and advances in algorithms. Although machine learning has not yet been implemented in every single field of underwater acoustics, it will be used more actively in the future in line with the ongoing development and overwhelming achievements of this method. To understand the research trends of machine learning applications in underwater acoustics, the general theoretical background of several related machine learning techniques is introduced in this paper.
Keywords
Underwater acoustics; Sonar system; Machine learning; Deep learning; Signal processing; Probabilistic model;
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1 Dempster, A.P., Laird, N.M., & Rubin, D.B. (1977). Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society, B, 1-38. https://doi.org/10.1111/j.2517-161.1977.tb01600.x
2 Diesing, M., Green, S.L., Stephens, D., Lark, R.M., Stewart, H.A., & Dove, D. (2014). Mapping Seabed Sediment: Comparison of Manual, Geostatistical, Object-based Image Analysis and Machine Learning Approaches. Continental Shelf Research, 84, 107-119. https://doi.org/10.1016/j.csr.2014.05.004   DOI
3 Elad, M. (2010). Sparse and Redundant Representations. New York: Springer.
4 Fischell, E.M., & Schmidt, H. (2015). Classification of Underwater Targets from Autonomous Underwater Vehicle Sampled Bistatic Acoustic Scattered Fields. The Journal of the Acoustical Society of America, 138(6), 3773-3784. https://doi.org/10.1121/1.4938017   DOI
5 Fukushima, K. (1980). Neocognition: A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position. Biological Cybernetics, 36(4), 193-202. https://doi.org/10.1007/BF00344251   DOI
6 Gemba, K.L., Nannuru, S., & Gerstoft, P. (2019). Robust Ocean Acoustic Localization with Sparse Bayesian Learning. IEEE Journal of Selected Topics in Signal Processing, 13(1), 49-60. https://doi.org/10.1109/JSTSP.2019.2900912   DOI
7 Gerstoft, P., Mecklenbrauker, C.F., Xenaki, A., & Nannuru, S. (2016). Multisnapshot Sparse Bayesian Learning for DOA. IEEE Signal Processing Letters, 23(10), 1469-1473. https://doi.org/10.1109/LSP.2016.2598550   DOI
8 Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep Learning. Cambridge, MA, USA: MIT Press.
9 Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference and Prediction (2nd ed). Springer.
10 Ke, X., Yuan, F., & Cheng, E. (2018). Underwater Acoustic Target Recognition Based on Supervised Feature-Separation Algorithm. Sensors, 18(12), 4318. https://doi.org/10.3390/s18124318   DOI
11 Kohavi, R. (1995). A Study of Cross-validation and Bootstrap for Accuracy Estimation and Model-selection. Proceedings of the International Joint Conference on Artificial Intelligence, 14(2), 1137-1145.
12 Learn OpenCV. (2018). Support Vector Machines (SVM). Retrieved 06 February 2020 from https://www.learnopencv.com/supportvector-machines-svm
13 LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86(11), 2278-2324. https://doi.org/10.1109/5.726791   DOI
14 MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability. 1(14), 281-297.
15 McLachlan, G.J., Lee, S.X., & Rathnayake, S.I. (2019). Finite Mixture Models. Annual Review of Statistics and Its Application, 6, 355-378. https://doi.org/10.1146/annurev-statistics-031017-100325   DOI
16 Murphy, K. (2012). Machine Learning: a Probabilistic Perspective (1st ed). Cambridge, MA, USA, MIT Press.
17 Murphy, S.M., & Hines, P.C. (2014). Examining the Robustness of Automated Aural Classification of Active Sonar Echoes. The Journal of the Acoustical Society of America, 135(2), 626-636. https://doi.org/10.1121/1.4861922   DOI
18 Nannuru, S., Gemba, K.L., Gerstoft, P., Hodgkiss, W.S., & Mecklenbrauker, C.F. (2019). Sparse Bayesian Learning with Multiple Dictionaries. Signal Processing, 159, 159-170. https://doi.org/10.1016/j.sigpro.2019.02.003   DOI
19 Tosic, I., & Frossard, P. (2011). Dictionary Learning. IEEE Signal Process. Magazine, 28(2), 27-38. https://doi.org/10.1109/MSP.2010.939537   DOI
20 Rumelhart, D.E., Hinton, G.E., & Williams, R.J. (1986). Learning Representations by Back-propagating Errors. Nature, 323, 533-536. https://doi.org/10.1038/323533a0   DOI
21 Saha, S. (2018). A Comprehensive Guide to Convolutional Neural Networks - the ELI5 Way. Towards Data Science. Retrieved 06 February 2020 from https://towardsdatascience.com/acomprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53
22 Wang, X., Liu, A., Zhang, Y., & Xue, F. (2019). Underwater Acoustic Target Recognition: A Combination of Multi-Dimensional Fusion Features and Modified Deep Neural Network. Remote Sensing, 11(16), 1888. https://doi.org/10.3390/rs11161888   DOI
23 Wipf, D.P., & Rao, B.D. (2004). Sparse Bayesian Learning for Basis Selection. IEEE Transactions on Signal Processing, 52(8), 2153-2164. https://doi.org/10.1109/TSP.2004.831016   DOI
24 Young, V.W., & Hines, P.C. (2007). Perception-based Automatic Classification of Impulsive-source Active Sonar Echoes. The Journal of the Acoustical Society of America, 122(3), 1502-1517. https://doi.org/10.1121/1.2767001   DOI
25 Buscombe, D., & Grams, P.E. (2018). Probabilistic Substrate Classification with Multispectral Acoustic Backscatter: A Comparison of Discriminative and Generative Models. Geoscience, 8(11), 395. https://doi.org/10.3390/geosciences8110395   DOI
26 Allen, N., Hines, P.C., & Young, V.W. (2011). Performances of Human Listeners and an Automatic Aural Classifier in Discriminating between Sonar Target Echoes and Clutter. The Journal of the Acoustical Society of America, 130(3), 1287-1298. https://doi.org/10.1121/1.3614549   DOI
27 Guest Blog. (2016). The Evolution and Core Concepts of Deep Learning & Neural Networks. Analytics Vidhya. Retrieved 06 February 2020 from https://www.analyticsvidhya.com/blog/2016/08/evolution-core-concepts-deep-learning-neural-networks
28 Bishop, C.M. (2006). Pattern Recognition and Machine Learning. Springer.
29 Choi, J., Choo, Y., & Lee, K. (2019). Acoustic Classification of Surface and Underwater Vessels in the Ocean Using Supervised Machine Learning. Sensors, 19(16), 3492. https://doi.org/10.3390/s19163492   DOI
30 Das, A. (2017). Theoretical and Experimental Comparison of Off-grid Sparse Bayesian Direction-of-arrival Estimation Algorithms. IEEE Access, 5, 18075-18087. https://doi.org/10.1109/ACCESS.2017.2747153   DOI
31 Das, A., & Sejnowski, T.J. (2017). Narrowband and Wideband Off-grid Direction-of-arrival Estimation via Sparse Bayesian Learning. IEEE Journal of Oceanic Engineering, 43(1), 108-118. https://doi.org/10.1109/JOE.2017.2660278   DOI