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Transfer learning for efficient classification of grouper sound.

Ali K Ibrahim1, Hanqi Zhuang2, Laurent M Chérubin1

  • 1Harbor Branch Oceanographic Institute, Florida Atlantic University, 5600 US1 North, Fort Pierce, Florida 34946, USA.

The Journal of the Acoustical Society of America
|October 2, 2020
PubMed
Summary

Transfer learning effectively classifies grouper species by their unique courtship sounds, even distinguishing them from vessel noise. This advanced method surpasses traditional feature identification for bioacoustics analysis.

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Area of Science:

  • Marine bioacoustics
  • Computational biology
  • Deep learning applications

Background:

  • Grouper species identification is crucial for fisheries management and conservation.
  • Understanding grouper spawning behavior requires accurate species classification.
  • Automated sound analysis offers a non-invasive method for studying marine life.

Purpose of the Study:

  • To develop and evaluate a transfer learning model for classifying grouper species based on their courtship sounds.
  • To investigate the inclusion of vessel sounds for identifying human interactions.
  • To compare the performance of deep learning models with traditional feature extraction methods.

Main Methods:

  • Grouper courtship sounds and vessel sounds were recorded during spawning aggregations.
  • Sounds were converted into time-frequency representations (spectrograms and scalograms).
  • Pretrained deep neural network models (VGG16, VGG19, GoogleNet, MobileNet) were utilized for image-based classification.

Main Results:

  • Transfer learning significantly improved grouper sound classification accuracy compared to manual feature identification.
  • Both spectrograms and scalograms yielded comparable classification performance.
  • The study demonstrated the potential for automated species identification using acoustic data.

Conclusions:

  • Transfer learning provides a robust and accurate method for classifying grouper species by their acoustic signals.
  • Acoustic monitoring can aid in understanding grouper spawning ecology and potential human impacts.
  • Deep learning models offer a promising avenue for advancing marine bioacoustic research.