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SKANN: Selective Kernel Audio Neural Networks for Underwater Mixed Ship Event Detection
Shan, Chun Zou, Tongyi Zhao, Lingjun Zhang, Qinnan Zhu, Yafeng Mohsen, Guizani Qiu, Jing
Shan, Chun
Zou, Tongyi
Zhao, Lingjun
Zhang, Qinnan
Zhu, Yafeng
Mohsen, Guizani
Qiu, Jing
Supervisor
Department
Machine Learning
Embargo End Date
Type
Journal article
Date
2025
License
Language
English
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Abstract
Underwater acoustic target recognition (UATR) has become increasingly prevalent for ocean detection, localisation, and identification. However, due to the complexity and variability of underwater environments, especially in multi ship event environments, where multiple acoustic signals coexist, practical applications face significant challenges. These challenges hinder single-category acoustic recognition algorithms, particularly in extracting time series features and achieving fine-grained or multi-scale feature fusion. This paper innovatively introduce the SKANN framework, which achieve precise submarine sound recognition in underwater mixed ship events environments through timing data enhancement and sampling training module and selective kernel feature extraction module. The timing data enhancement and sampling training module improves time sequence feature extraction through progressive acoustic sampling. The selective kernel feature extraction module effectively fuses multi-scale features by integrating selective kernel (SK) technology. To simulate concurrent ship events, we constructed the mixed ship noise dataset (M-DeepShip), providing an experimental basis and test platform for underwater mixed ship event detection. This dataset ensures that the model encounters diverse audio samples during training and validation, improving its ability to extract temporal features. Experimental results show that SKANN achieves a 93.6% recognition rate on the M-DeepShip dataset, demonstrating its effectiveness in recognising underwater mixed ship events. Given the complexity of real underwater environments, this work lays a crucial foundation for the sound recognition of submarine vessels. Future research will focus on real marine environments to validate and refine the models and methods for practical applications.
Citation
C. Shan et al., “SKANN: Selective Kernel Audio Neural Networks for Underwater Mixed Ship Event Detection,” CAAI Trans Intell Technol, vol. 10, no. 5, pp. 1548–1558, Oct. 2025, doi: 10.1049/CIT2.70037
Source
CAAI Transactions on Intelligence Technology
Conference
Keywords
Deep learning, neural network, signal detection, signal processing
Subjects
Source
Publisher
Wiley