R. A. McCarthy, Y. Zhang, S. A. Verburg, W. F. Jenkins, and P. Gerstoft, “Machine learning in acoustics: a review and open-source repository,” npj Acoust. 1, 18 (2025).
10.1038/s44384-025-00021-wS. A. Verburg, E. Fernandez-Grande, and P. Gerstoft, “Differentiable physics for sound field reconstruction,” J. Acoust. Soc. Am. 158, 4059-4069 (2025).
10.1121/10.0039862Y. Sun, L. Cheng, J. Li, and P. Gerstoft, “Hankel-FNO: Fast underwater acoustic charting via physics-encoded Fourier neural operator,” J. Acoust. Soc. Am. 158, 5075-5089 (2025).
10.1121/10.0041890R. A. McCarthy, S. T. Merrifield, J. Sarkar, R. Bednar, A. Nager, C. Brooks, D. Ung, J. Donohoe, and E. J. Terrill, “Machine learning transmission loss predictions in acoustic field experiments,” IEEE J. Ocean. Eng. 50, 1668-1675 (2025).
10.1109/JOE.2024.3498007I. K. Deo, A. Venkateshwaran, and R. K. Jaiman, “Predicting transmission loss in underwater acoustics using continual learning with range-dependent conditional convolutional neural networks,” J. Acoust. Soc. Am. 157, 3930-3945 (2025).
10.1121/10.0036773H. Niu and P. Gerstoft, “Source localization in underwater waveguides using machine learning,” J. Acoust. Soc. Am. 140, 3232 (2016).
10.1121/1.4970220J. A. Castro-Correa, M. Badiey, J. H. Giraldo, and F. D. Malliaros, “Semi-supervised graph learning for underwater source localization using ship-of-opportunity spectrograms,” J. Acoust. Soc. Am. 158, 1836-1848 (2025).
10.1121/10.0039042Y. Liu, W. Zhang, J. Shi, P. Gerstoft, H. Niu, Q. Yu, and Z. Meng, “Bayesian optimization-tuned machine learning for underwater acoustic target localization,” J. Acoust. Soc. Am. 158, 4070-4086 (2025).
10.1121/10.0039891M. Goldwater, D. P. Zitterbart, D. Wright, and J. Bonnel, “Machine-learning-based simultaneous detection and ranging of impulsive baleen whale vocalizations using a single hydrophone,” J. Acoust. Soc. Am. 153, 1094-1107 (2023).
10.1121/10.0017118A. Vardi, P. H. Dahl, D. Dall’Osto, D. Knobles, P. Wilson, J. Leonard, and J. Bonnel, “Estimation of the spatial variability of the New England Mud Patch geoacoustic properties using a distributed array of hydrophones and deep learning,” J. Acoust. Soc. Am. 156, 4229-4241 (2024).
10.1121/10.0034707A. Varon, J. Mars, and J. Bonnel, “Approximation of modal wavenumbers and group speeds in an oceanic waveguide using a neural network,” JASA Express Lett. 3, 066003 (2023).
10.1121/10.0019704Z.-H. Michalopoulou and P. Gerstoft, “Inversion in an uncertain ocean using Gaussian processes,” J. Acoust. Soc. Am. 153, 1600-1611 (2023).
10.1121/10.0017437J. Jin, P. Saha, N. Durofchalk, S. Mukhopadhyay, J. Romberg, and K. G. Sabra, “Machine learning approaches for ray-based ocean acoustic tomography,” J. Acoust. Soc. Am. 152, 3768-3788 (2022).
10.1121/10.0016498S. Yoon, Y. Park, P. Gerstoft, and W. Seong, “Predicting ocean pressure field with a physics-informed neural network,” J. Acoust. Soc. Am. 155, 2037-2049 (2024).
10.1121/10.0025235S. Yoon, Y. Park, K. Lee, and W. Seong, “Physics-informed neural networks in support of modal wavenumber estimation,” J. Acoust. Soc. Am. 156, 2275-2286 (2024).
10.1121/10.0030461Y. Park, “Physics-informed machine learning for matched field source-range estimation,” J. Acoust. Soc. Am. 158, 4623-4636 (2025).
10.1121/10.0041850F. B. Jensen, W. A. Kuperman, M. B. Porter, and H. Schmidt, Computational Ocean Acoustics (Springer, New York, 2011), pp. 65-153.
10.1007/978-1-4419-8678-8_2- Publisher :The Acoustical Society of Korea
- Publisher(Ko) :한국음향학회
- Journal Title :The Journal of the Acoustical Society of Korea
- Journal Title(Ko) :한국음향학회지
- Volume : 45
- No :4
- Pages :445-458
- Received Date : 2026-06-04
- Revised Date : 2026-07-03
- Accepted Date : 2026-07-06
- DOI :https://doi.org/10.7776/ASK.2026.45.4.445



The Journal of the Acoustical Society of Korea









