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Classification of Surface Vehicle Propeller Cavitation Noise Using Spectrogram Processing in Combination with

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This study enhances vehicle propeller cavitation noise detection using an improved Detection Envelope Modulation On Noise (DEMON) algorithm and a modified Convolution Neural Network (CNN). The method achieves over 90% accuracy in classifying underwater noise.

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Area of Science:

  • Underwater acoustics
  • Signal processing
  • Machine learning for acoustics

Background:

  • Surface vehicle propeller cavitation noise (VPCN) poses challenges for detection and classification in shallow waters.
  • Traditional methods often struggle with the quality and complexity of VPCN signals.
  • Accurate VPCN identification is crucial for naval applications and environmental monitoring.

Purpose of the Study:

  • To develop an enhanced method for detecting and classifying VPCN in shallow water.
  • To improve the signal-to-noise ratio and accuracy of VPCN analysis.
  • To leverage advanced signal processing and deep learning techniques for superior performance.

Main Methods:

  • Utilized an improved Detection Envelope Modulation On Noise (DEMON) algorithm to analyze amplitude variation (AV) and detect fundamental frequencies of VPCN signals.
  • Adapted the sliding window size of a Convolution Neural Network (CNN) to suit VPCN spectrogram data characteristics.
  • Reconstructed the CNN layer structure to optimize feature extraction and classification.

Main Results:

  • Successfully detected fundamental frequencies within VPCN spectrogram data.
  • The proposed DEMON-CNN method achieved a VPCN classification accuracy exceeding 90% on measured data.
  • Demonstrated superior performance compared to traditional VPCN detection and classification approaches.

Conclusions:

  • The combined DEMON and modified CNN approach significantly enhances the quality and accuracy of VPCN detection and classification.
  • This method offers a robust solution for analyzing underwater acoustic noise in challenging shallow-water environments.
  • The findings provide a valuable advancement for underwater acoustic surveillance and noise analysis technologies.