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<p>Vessel recognition based on hydroacoustic signals is an important research area. The marine environment is complex and variable, which makes the transmission and reception process of the signals have some random cases. At the same time, there are various interference and noise sources in the water, such as waves, underwater equipment, marine organisms, etc., which bring difficulties to the identification and analysis of vessel targets. This paper proposed a model named Emphasized Dimension Attention and Future Fusion-Time Delay Neural Network (EDAFF-TDNN). The model adjusts the weights of the feature map dynamically by learning the correlation between dimensions through Squeeze and Excitation Block (SE-Block), which enables the model to capture the contextual information, thus the model performance is improved. The mechanism of feature fusion is also introduced to extract multi-layer features to improve the feature representation capability. The attention mechanism is added on top of TDNN. By considering the differences of each feature dimension, it enables the model to focus on the key information when learning feature representations. Which improves the model performance in complex scenarios. In addition, experiments of the model on the ShipsEar dataset show a recognition accuracy of 98.2%.</p>

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Underwater vessel sound recognition based on multi-layer feature and attention mechanism

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月2日

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  • Wei Wei,
  • Jing Li,
  • Yucheng Han,
  • Lili Zhang,
  • Ning Cui,
  • Pei Yu,
  • Hongxin Tan,
  • Xudong Yang,
  • Kang Yang

摘要

Vessel recognition based on hydroacoustic signals is an important research area. The marine environment is complex and variable, which makes the transmission and reception process of the signals have some random cases. At the same time, there are various interference and noise sources in the water, such as waves, underwater equipment, marine organisms, etc., which bring difficulties to the identification and analysis of vessel targets. This paper proposed a model named Emphasized Dimension Attention and Future Fusion-Time Delay Neural Network (EDAFF-TDNN). The model adjusts the weights of the feature map dynamically by learning the correlation between dimensions through Squeeze and Excitation Block (SE-Block), which enables the model to capture the contextual information, thus the model performance is improved. The mechanism of feature fusion is also introduced to extract multi-layer features to improve the feature representation capability. The attention mechanism is added on top of TDNN. By considering the differences of each feature dimension, it enables the model to focus on the key information when learning feature representations. Which improves the model performance in complex scenarios. In addition, experiments of the model on the ShipsEar dataset show a recognition accuracy of 98.2%.