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Underwater sound classification using learning based methods: A review
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DOI：10.1016/j.eswa.2024.124498.png)
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Abstract
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Underwater sound classification has been an area of interest in the research community because of its applications in military, commercial, and environmental domains. Underwater sound classification is a challenging task because of the high background noise and complex sound propagation patterns in the sea environment. For underwater sound classification, deterministic as well as stochastic techniques are being used. However, in recent years, stochastic techniques which are learning -based are getting a lot of attention. There exist few survey studies with a limited scope that cover the limited number of studies. In this study, we present the most comprehensive review of research and the latest developments in the field of underwater sound classification by highlighting the contributions and challenges from over 250 recent research papers. We discuss machine learning as well as deep learning -based methods for marine vessel sound classification and fish sound classification. The study also includes details of sources of underwater sound, features, classifiers, datasets, related techniques, challenges, and future trends. We hope that the study will benefit the general reader as well as the research community to have a complete picture of the latest research in the field.
Keywords:
Underwater audio classification
Ship classification
Fish classification
Underwater sound
Machine learning
Deep learning
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