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Ship Type Classification by Convolutional Neural Networks with Auditory-like Mechanisms.

Sheng Shen1, Honghui Yang1, Xiaohui Yao1

  • 1School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China.

Sensors (Basel, Switzerland)
|January 8, 2020
PubMed
Summary
This summary is machine-generated.

This study presents a novel convolutional neural network for ship type classification using radiated noise. The auditory-inspired model achieved 87.2% accuracy in identifying four ship types and ocean background noise.

Keywords:
machine learningneural networkship radiated noiseunderwater acoustics

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Area of Science:

  • Marine acoustics
  • Signal processing
  • Artificial intelligence

Background:

  • Ship radiated noise monitoring is crucial for hydrophone site management.
  • Accurate ship type classification aids in understanding underwater acoustic environments.

Purpose of the Study:

  • To develop an advanced convolutional neural network for ship type classification using radiated noise.
  • To incorporate auditory-like mechanisms for enhanced feature extraction from hydrophone signals.

Main Methods:

  • A novel convolutional neural network model featuring cochlea and auditory center components.
  • Utilizing time convolutional layers with auditory filters and dilated convolutions for signal decomposition.
  • Employing a time-frequency conversion layer and deep architecture with multistage auditory mechanisms for feature extraction.

Main Results:

  • The proposed model achieved an 87.2% classification accuracy for four ship types and ocean background noise.
  • Demonstrated effective extraction of auditory features from raw hydrophone signals.
  • Successfully identified ship types under diverse working conditions.

Conclusions:

  • The auditory-inspired convolutional neural network offers significant improvements for ship type classification.
  • The model's architecture effectively processes hydrophone signals to identify ship types.
  • This approach enhances underwater acoustic monitoring capabilities.