A survey on machine learning in ship radiated noise

The utilization of machine learning in analyzing ship radiated noise (SR-N) is undergoing rapid evolution. Because the omnipresent background noise strongly depends on the highly variable environment, the application of such techniques poses challenges. Furthermore, publicly available labeled datasets are scarce. Motivated by this, there has been a surge in the number of publications regarding the implementation of machine learning in the monitoring of SR-N within the past few years. This comprehensive survey delineates the state-of-the-art machine learning techniques applied to SR-N, with a specific focus on passive measurements. Recent developments are categorized into several sub-areas, namely; publicly available datasets, data augmentation, signal denoising, feature extraction, detection, localization, and recognition of SR-N. Additionally, future research directions are explored.

• Record URL:
  • https://doi.org/10.1016/j.oceaneng.2024.117252
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0029801824005894
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00298018
• Supplemental Notes:
  • © 2024 The Authors. Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
• Authors:
  • Hummel, Hilde I
  • van der Mei, Rob
  • Bhulai, Sandjai
• Publication Date: 2024-4-15

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 117252
• Serial:
  • Ocean Engineering
  • Volume: 298
  • Issue Number: 0
  • Publisher: Pergamon
  • ISSN: 0029-8018
  • EISSN: 1873-5258
  • Serial URL: http://www.sciencedirect.com/science/journal/00298018

Subject/Index Terms

• TRT Terms: Acoustic radiation; Machine learning; Noise; Ships; Surveys; Underwater sound
• Subject Areas: Environment; Marine Transportation; Vehicles and Equipment;

Filing Info

• Accession Number: 01911924
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 15 2024 4:34PM