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Efficient ship noise classification with positive incentive noise and fused features using a simple convolutional

Xu Lin1, Ruichun Dong2, Yuqing Zhao2

  • 1College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China. linxu@sdust.edu.cn.

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This study introduces positive incentive noise to improve ship noise analysis in hydroacoustic remote sensing. Simple CNN models achieve high performance, overcoming data scarcity for underwater robot applications.

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Area of Science:

  • Hydroacoustic remote sensing
  • Underwater acoustics
  • Signal processing

Background:

  • Ship noise analysis is vital for identifying vessels using hydroacoustic remote sensing.
  • Data scarcity challenges the development of accurate ship noise classification models.
  • Existing methods often rely on complex network structures unsuitable for resource-limited underwater robots.

Purpose of the Study:

  • To address data scarcity in hydroacoustic signal recognition.
  • To develop a simple yet effective Convolutional Neural Network (CNN) based recognition method.
  • To enable accurate ship identification for underwater robotic applications with limited computing power.

Main Methods:

  • Introduced positive incentive noise to augment limited datasets.
  • Employed a CNN-based architecture for hydroacoustic signal recognition.
  • Evaluated various feature extraction techniques and compared performance against prior studies.

Main Results:

  • The proposed CNN method achieved performance comparable or superior to previous studies.
  • Simple neural networks demonstrated high performance and excellent generalizability.
  • Effective feature extraction was achieved even with the addition of noise.

Conclusions:

  • Simple neural networks can effectively classify hydroacoustic signals without complex architectures.
  • Positive incentive noise is a viable strategy to mitigate data scarcity issues.
  • The developed method is suitable for real-world applications on underwater robots.