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Multiclass CNN Approach for Automatic Classification of Dolphin Vocalizations.

Francesco Di Nardo1, Rocco De Marco2, Daniel Li Veli2

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This study introduces a new method using convolutional neural networks (CNNs) to classify dolphin sounds from passive acoustic monitoring (PAM) data. The CNN achieved high accuracy, improving our ability to monitor dolphin populations and reduce human-wildlife conflict.

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Area of Science:

  • Marine biology
  • Bioacoustics
  • Artificial Intelligence

Background:

  • Passive Acoustic Monitoring (PAM) is crucial for tracking marine mammals like dolphins.
  • Understanding dolphin vocalizations is key to assessing ecosystem health and human impact.
  • Current methods for analyzing dolphin sounds can be labor-intensive and require expertise.

Purpose of the Study:

  • To develop and evaluate a novel approach for classifying common bottlenose dolphin vocalizations using Convolutional Neural Networks (CNNs).
  • To improve the accuracy and efficiency of analyzing underwater acoustic recordings for dolphin monitoring.
  • To enhance species conservation efforts and mitigate human-fisheries conflict through better acoustic monitoring.

Main Methods:

  • Utilized a dataset of nearly 10,000 spectrograms from common bottlenose dolphin (Tursiops truncatus) recordings.
  • Applied edge-detection filters to spectrograms to reduce noise and improve feature extraction.
  • Trained and tested a CNN model using a 10-fold cross-validation procedure for robust performance evaluation.

Main Results:

  • The CNN model achieved an overall average accuracy of 95.2% and an F1-score of 87.8%.
  • High class-specific accuracies were recorded: whistles (97.9%), echolocation clicks (94.5%), feeding buzzes (94.0%), and burst pulse sounds (92.3%).
  • The F1-score for whistles exceeded 95%, with other vocalization types maintaining scores above 80%.

Conclusions:

  • The developed CNN-based method offers a highly accurate and promising tool for classifying dolphin vocalizations from PAM data.
  • This approach significantly enhances the capabilities of passive acoustic monitoring for marine mammal research.
  • The findings support improved dolphin conservation strategies and the mitigation of conflicts between dolphins and fisheries.