A Two-Stream Network for Underwater Acoustic Target Classification

Xing, Guanghui; Liu, Peishun; Zhang, Hui; Tang, Ruichun; Yin, Yaguang

doi:10.1145/3449301.3449343

2020 6th International Conference on Robotics and Artificial Intelligence 2020

DOI: 10.1145/3449301.3449343

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A Two-Stream Network for Underwater Acoustic Target Classification

Guanghui Xing

Peishun Liu

Hui Zhang

et al.

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2024

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Cited by 2 publications

References 16 publications

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A survey on machine learning in ship radiated noise

Hummel,

van der Mei,

Bhulai

2024

Ocean Engineering

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A survey on machine learning in ship radiated noise

Hummel,

van der Mei,

Bhulai

2024

Ocean Engineering

View full text Add to dashboard Cite

A Novel Multi-Feature Fusion Model Based on Pre-Trained Wav2vec 2.0 for Underwater Acoustic Target Recognition

Pu,

Zhang,

Xue

et al. 2024

Remote Sensing

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Although recent data-driven Underwater Acoustic Target Recognition (UATR) methods have played a dominant role in marine acoustics, they suffer from complex ocean environments and rather small datasets. To tackle such challenges, researchers have resorted to transfer learning in an effort to fulfill UATR tasks. However, existing pre-trained models are trained on audio speech data, and are not suitable for underwater acoustic data. Therefore, it is necessary to make further optimization on the basis of these models to make them suitable for the UATR task. Here, we propose a novel UATR framework called Attention Layer Supplement Integration (ALSI), which integrates large pre-trained neural networks with customized attention modules for acoustic. Specifically, the ALSI model consists of two important modules, namely Scale ResNet and Residual Hybrid Attention Fusion (RHAF). First, the Scale ResNet module takes the Constant-Q transform feature as input to obtain relatively important frequency information. Next, RHAF takes the temporal feature extracted by wav2vec 2.0 and the frequency feature extracted by Scale ResNet as input and aims to better integrate the time–frequency features with the temporal feature by using the attention mechanism. The RHAF module can help wav2vec 2.0, which is trained on speech data, to better adapt to underwater acoustic data. Finally, the experiments on the ShipsEar dataset demonstrated that our model can achieve recognition accuracy of 96.39%. In conclusion, extensive experiments confirm the effectiveness of our model on the UATR task.

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A Two-Stream Network for Underwater Acoustic Target Classification

Cited by 2 publications

References 16 publications

A survey on machine learning in ship radiated noise

A survey on machine learning in ship radiated noise

A Novel Multi-Feature Fusion Model Based on Pre-Trained Wav2vec 2.0 for Underwater Acoustic Target Recognition

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