Oceanship: A Large-Scale Dataset for Underwater Audio Target Recognition
arxiv(2024)
摘要
The recognition of underwater audio plays a significant role in identifying a
vessel while it is in motion. Underwater target recognition tasks have a wide
range of applications in areas such as marine environmental protection,
detection of ship radiated noise, underwater noise control, and coastal vessel
dispatch. The traditional UATR task involves training a network to extract
features from audio data and predict the vessel type. The current UATR dataset
exhibits shortcomings in both duration and sample quantity. In this paper, we
propose Oceanship, a large-scale and diverse underwater audio dataset. This
dataset comprises 15 categories, spans a total duration of 121 hours, and
includes comprehensive annotation information such as coordinates, velocity,
vessel types, and timestamps. We compiled the dataset by crawling and
organizing original communication data from the Ocean Communication Network
(ONC) database between 2021 and 2022. While audio retrieval tasks are
well-established in general audio classification, they have not been explored
in the context of underwater audio recognition. Leveraging the Oceanship
dataset, we introduce a baseline model named Oceannet for underwater audio
retrieval. This model achieves a recall at 1 (R@1) accuracy of 67.11
recall at 5 (R@5) accuracy of 99.13
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