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Auditory Inspired Convolutional Neural Networks for Ship Type Classification with Raw Hydrophone Data.

Sheng Shen1, Honghui Yang1, Junhao Li1

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

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|December 3, 2020
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Summary
This summary is machine-generated.

This study introduces an auditory-inspired neural network for ship detection and classification using underwater acoustics. The novel approach enhances accuracy in identifying ship noise, contributing to reduced shipping noise pollution.

Keywords:
auditoryconvolutional neural networkdeep learninghydrophoneship radiated noise

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Area of Science:

  • Acoustics
  • Signal Processing
  • Machine Learning

Background:

  • Underwater radiated noise from shipping impacts marine ecosystems.
  • Effective detection and classification of ship noise are crucial for mitigation strategies.
  • Conventional methods require improvement for accurate ship noise analysis.

Purpose of the Study:

  • To develop an auditory-inspired convolutional neural network (CNN) for ship detection and classification from raw underwater acoustic signals.
  • To improve the accuracy of classifying different ship types based on their radiated noise.
  • To provide practical guidelines for reducing the underwater noise footprint of shipping.

Main Methods:

  • Utilized a multi-scale 1D time convolutional layer initialized with auditory filter banks for signal decomposition.
  • Employed permute and energy pooling layers to transform signals into a frequency domain representation mimicking the auditory cortex.
  • Applied 2D frequency convolutional layers to identify spectro-temporal patterns and optimize auditory filters through classification-based objective functions.

Main Results:

  • Achieved an overall classification accuracy of 79.2% for five ship types and background noise.
  • Demonstrated a 6% improvement in accuracy compared to conventional approaches.
  • Showcased adaptive auditory filter banks that enhance classification performance.

Conclusions:

  • The proposed auditory-inspired CNN effectively detects and classifies ship radiated noise.
  • The model's optimization process reflects the plasticity of the auditory system, leading to improved feature representations.
  • This approach offers a promising method for monitoring and mitigating underwater shipping noise.