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Segmentation Method for Ship-Radiated Noise Using the Generalized Likelihood Ratio Test on an Ordinal Pattern

Lei He1, Xiao-Hong Shen1, Mu-Hang Zhang1

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

Entropy (Basel, Switzerland)
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Summary

This study introduces a novel audio segmentation method for ship-radiated noise (SRN) using generalized likelihood ratio (GLR) tests on ordinal pattern distribution (OPD). The new approach accurately identifies change-points in SRN, improving ship status identification.

Keywords:
audio segmentationchange-point detectionordinal patternship-radiated noise

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Area of Science:

  • Signal Processing
  • Acoustics
  • Machine Learning

Background:

  • Ship-radiated noise (SRN) segmentation is crucial for identifying ship statuses.
  • Existing methods struggle with SRN diversity due to a lack of prior knowledge.
  • Acoustic feature extraction and probability distribution estimation pose challenges for SRN analysis.

Purpose of the Study:

  • To develop a robust audio segmentation method for ship-radiated noise (SRN).
  • To improve the accuracy of single change-point detection (SCPD) and multiple change-points detection (MCPD) in SRN.
  • To provide a more effective segmentation criterion for ship classification.

Main Methods:

  • Proposed a segmentation criterion based on a generalized likelihood ratio (GLR) test applied to ordinal pattern distribution (OPD).
  • Introduced the GLR-OPD method for single change-point detection (SCPD) and multiple change-points detection (MCPD).
  • Efficiently estimated OPD using the sequential structure of ordinal patterns across analysis windows, avoiding feature extraction and distribution estimation.

Main Results:

  • The proposed method accurately estimates the number and location of change-points in synthetic signals.
  • Segmentation results on real-world SRN demonstrate more distinguishable segments compared to Bayesian Information Criterion (BIC) methods.
  • The method proves effective for SRN segmentation, enhancing subsequent classification.

Conclusions:

  • The GLR-OPD method offers a significant advancement in ship-radiated noise segmentation.
  • This approach overcomes limitations of existing methods by not requiring prior knowledge or complex feature extraction.
  • The enhanced segmentation capability directly contributes to more accurate ship status and category identification.