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Sensor-Oriented Framework for Underwater Acoustic Signal Classification Using EMD-Wavelet Filtering and

Sergii Babichev1,2, Oleg Yarema3, Yevheniy Khomenko1

  • 1Department of Physics, Kherson State University, 73008 Kherson, Ukraine.

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Summary

This study introduces an automated pipeline for ship acoustic signal classification, enhancing vessel identification and maritime security. The method improves accuracy and noise suppression for real-time applications.

Keywords:
bayesian optimizationempirical mode decompositionfeature extractionnon-stationary signal processingrandom forestship acoustic signal classificationunderwater acoustic sensorswavelet filtering

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Area of Science:

  • Marine acoustics
  • Signal processing
  • Machine learning

Background:

  • Ship acoustic signal classification is vital for maritime operations but challenged by signal noise and non-stationarity.
  • Existing methods often yield suboptimal accuracy due to these inherent signal complexities.

Purpose of the Study:

  • To develop an automated, robust pipeline for accurate ship acoustic signal classification.
  • To overcome limitations of traditional methods in handling noisy and non-stationary underwater acoustic data.

Main Methods:

  • Empirical Mode Decomposition (EMD) for signal decomposition and Intrinsic Mode Function (IMF) selection via Signal-to-Noise Ratio (SNR).
  • Adaptive wavelet filtering optimized using SNR and Stein's Unbiased Risk Estimate (SURE) for noise reduction.
  • Feature extraction (statistical, FFT, wavelet) followed by selection of top 11 features and Bayesian-optimized Random Forest classification with majority voting.

Main Results:

  • The pipeline achieved high classification accuracy and significantly improved noise suppression.
  • Demonstrated robust performance in identifying ship acoustic signals under challenging conditions.
  • The Bayesian optimization reduced computational complexity while enhancing classification precision.

Conclusions:

  • The proposed automated pipeline offers a scalable, efficient, and accurate solution for real-time ship acoustic signal classification.
  • This approach enhances maritime security and underwater navigation capabilities.
  • The integration of EMD, wavelet filtering, and optimized machine learning provides a powerful tool for acoustic signal analysis.