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A Bayesian binary algorithm for root mean squared-based acoustic signal segmentation.

Paulo Hubert1, Rebecca Killick2, Alexandra Chung3

  • 1Department of Mechanical Engineering, Escola Politecnica, University of São Paulo, São Paulo, SP, Brazil.

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Summary
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This study introduces a Bayesian algorithm for segmenting long acoustic signals by detecting changes in root-mean-square power. The method was successfully applied to marine sound data from Brazil.

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Area of Science:

  • Signal processing
  • Statistical modeling
  • Bioacoustics

Background:

  • Changepoint analysis identifies data changes in ordered vectors.
  • Existing methods cover mean, variance, and regression parameter shifts.
  • Segmentation of long acoustic signals requires specialized algorithms.

Purpose of the Study:

  • To develop and evaluate a Bayesian algorithm for segmenting long acoustic signals.
  • The algorithm focuses on detecting changes in root-mean-square power.
  • To test the algorithm's performance on real-world marine acoustic data.

Main Methods:

  • Investigated a Bayesian model with two parameterizations.
  • Proposed a binary segmentation algorithm in two versions (non-informative and informative priors).
  • Applied the algorithms to annotated acoustic signals from Alcatrazes marine park.

Main Results:

  • The proposed Bayesian algorithm effectively segments acoustic signals based on root-mean-square power changes.
  • Both versions of the binary algorithm demonstrated utility in signal segmentation.
  • Successful application to marine acoustic data from a Brazilian preservation park.

Conclusions:

  • The developed Bayesian approach offers a robust method for acoustic signal segmentation.
  • The algorithm's performance is validated on ecological acoustic data.
  • This technique aids in analyzing long-term acoustic monitoring data.