In this work, we explore machine learning through a model-agnostic feature representation known as braiding, that employs braid manifolds to interpret multipath ray bundles. We generate training and testing data using the well-known BELLHOP model to simulate shallow water acoustic channels across a wide range of multipath scattering activity. We examine three different machine learning techniques—k-nearest neighbors, random forest tree ensemble, and a fully connected neural network—as well as two machine learning applications. The first application applies known physical parameters and braid information to determine the number of reflections the acoustic signal may undergo through the environment. The second application applies braid path information to determine if a braid is an important representation of the channel (i.e., evolving across bands of higher amplitude activity in the channel). Testing accuracy of the best trained machine learning algorithm in the first application was 86.70% and the testing accuracy of the second application was 99.94%. This work can be potentially beneficial in examining how the reflectors in the environment changeover time while also determining relevant braids for faster channel estimation.
References
1.
Adavanne
, S.
, Politis
, A.
, and Virtanen
, T.
(2018
). “Direction of arrival estimation for multiple sound sources using convolutional recurrent neural network
,” in 2018 26th European Signal Processing Conference (EUSIPCO)
, pp. 1462
–1466
.2.
Artin
, E.
(1947
). “Theory of braids
,” Ann. Math.
48
(1
), 101
–126
.3.
Bianco
, M. J.
, Gerstoft
, P.
, Traer
, J.
, Ozanich
, E.
, Roch
, M. A.
, Gannot
, S.
, and Deledalle
, C.-A.
(2019
). “Machine learning in acoustics: Theory and applications
,” J. Acoust. Soc. Am.
146
(5
), 3590
–3628
.4.
Chen
, R.
, and Schmidt
, H.
(2021
). “Model-based convolutional neural network approach to underwater source-range estimation
,” J. Acoust. Soc. Am.
149
(1
), 405
–420
.5.
Choi
, J.
, Choo
, Y.
, and Lee
, K.
(2019
). “Acoustic classification of surface and underwater vessels in the ocean using supervised machine learning
,” Sensors
19
(16
), 3492
.6.
Hu
, G.
, Wang
, K.
, Peng
, Y.
, Qiu
, M.
, Shi
, J.
, and Liu
, L.
(2018
). “Deep learning methods for underwater target feature extraction and recognition
,” Comput. Intell. Neurosci.
2018
, 2143011
.7.
Huang
, J.
, Barbeau
, M.
, Blouin
, S.
, Hamm
, C.
, and Taillefer
, M.
(2017
). “Simulation and modeling of hydro acoustic communication channels with wide band attenuation and ambient noise
,” Int. J. Parallel Emerg. Distrib. Syst.
32
(5
), 466
–485
.8.
Jalal
, A.
, Salman
, A.
, Mian
, A.
, Shortis
, M.
, and Shafait
, F.
(2020
). “Fish detection and species classification in underwater environments using deep learning with temporal information
,” Ecological Inf.
57
, 101088
.9.
McCarthy
, R.
, and Sen Gupta
, A.
(2021
). “Underwater channel estimation exploiting multipath feature morphology
,” J. Acoust. Soc. Am.
149
(2
), 983
–996
.10.
Michalopoulou
, Z.-H.
, Pole
, A.
, and Abdi
, A.
(2019
). “Bayesian coherent and incoherent matched-field localization and detection in the ocean
,” J. Acoust. Soc. Am.
146
(6
), 4812
–4820
.11.
Niu
, H.
, Gong
, Z.
, Ozanich
, E.
, Gerstoft
, P.
, Wang
, H.
, and Li
, Z.
(2019
). “Deep-learning source localization using multi-frequency magnitude-only data
,” J. Acoust. Soc. Am.
146
(1
), 211
–222
.12.
Ozanich
, E.
, Gerstoft
, P.
, and Niu
, H.
(2020
). “A feedforward neural network for direction-of-arrival estimation
,” J. Acoust. Soc. Am.
147
(3
), 2035
–2048
.13.
Ping
, G.
, Fernandez-Grande
, E.
, Gerstoft
, P.
, and Chu
, Z.
(2020
). “Three-dimensional source localization using sparse Bayesian learning on a spherical microphone array
,” J. Acoust. Soc. Am.
147
(6
), 3895
–3904
.14.
Porter
, M.
(2011
). The BELLHOP Manual and Users Guide: PRELIMINARY DRAFT, Heat, Light, and Sound Research, Inc.15.
Qarabaqi
, P.
, and Stojanovic
, M.
(2013
). “Statistical characterization and computationally efficient modeling of a class of underwater acoustic communication channels
,” IEEE J. Oceanic Eng.
38
(4
), 701
–717
.16.
17.
Sen Gupta
, A.
, and Kirsteins
, I.
(2017
). “Disentangling sonar target features using braided feature graphs
,” in OCEANS 2017
, Anchorage, AK
, pp. 1
–5
.18.
Sen Gupta
, A.
, and McCarthy
, R.
(2018
). “Interpreting different features of shallow water acoustic channels using braid manifolds
,” 2018 Fourth Underwater Communications and Network Conference (UComms)
, Lerici, Italy
, pp. 1
–5
.19.
Sen Gupta
, A.
, and McCarthy
, R.
(2019
). “Employing braid theory to disentangle overlapping oceanic events in the shallow water acoustic channel
,” J. Acoust. Soc. Am.
145
(3
), 1884
–1884
.20.
Sen Gupta
, A.
, and Preisig
, J.
(2012
). “A geometric mixed norm approach to shallow water acoustic channel estimation and tracking
,” Phys. Commun.
5
, 119
–128
.21.
Shiu
, Y.
, Palmer
, K. J.
, Roch
, M. A.
, Fleishman
, E.
, Liu
, X.
, Nosal
, E.-M.
, Helble
, T.
, Cholewiak
, D.
, Gillespie
, D.
, and Klinck
, H.
(2020
). “Deep neural networks for automated detection of marine mammal species
,” Sci. Rep.
10
, 607
.22.
Siddiqui
, S.
, Malik
, I.
, Shafait
, F.
, Mian
, A.
, Shortis
, M.
, and Harvey
, E.
(2018
). “Automatic fish species classification in underwater videos: Exploiting pretrained deep neural network models to compensate for limited labelled data
,” ICES J. Mar. Sci.
75
, 374
–389
.23.
Thiffeault
, J.
(2010
). “Braids of entangled particle trajectories
,” Chaos: An Interdisciplinary J. Nonlin. Sci.
20
(1
), 017516
.24.
Wiley
, E.
, McNyset
, K. M.
, Peterson
, A. T.
, Robins
, C. R.
, and Stewart
, A. M.
(2003
). “Niche modeling perspective on geographic range predictions in the marine environment using a machine-learning algorithm
,” Oceanography
16
(3
), 120
–127
.25.
Williams
, D.
, and España
, A.
(2020
). “Toward explainable convolutional neural network classifiers with acoustic-color sonar data
,” J. Acoust. Soc. Am.
148
(4
), 2661
–2661
.26.
Wu
, H.
, Song
, Q.
, and Jin
, G.
(2018
). “Deep learning based framework for underwater acoustic signal recognition and classification
,” in Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence, CSAI '18
, Association for Computing Machinery
, New York, NY, USA
, pp. 385
–388
.27.
Yue
, H.
, Zhang
, L.
, Wang
, D.
, Wang
, Y.
, and Lu
, Z.
(2017
). “The classification of underwater acoustic targets based on deep learning methods
,” Adv. Intell. Syst. Res.
134
, 526
–529
.28.
Xinhua
, Z.
, Zhenbo
, L.
, and Chunyu
, K.
(2003
). “Underwater acoustic targets classification using support vector machine
,” in Proceedings of the 2003 International Conference on Neural Networks and Signal Processing
, Vol. 2
, pp. 932
–935
.29.
Zheng
, Z.
, Yang
, T. C.
, Gerstoft
, P.
, and Pan
, X.
(2020
). “Joint towed array shape and direction of arrivals estimation using sparse Bayesian learning during maneuvering
,” J. Acoust. Soc. Am.
147
(3
), 1738
–1751
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