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Related Experiment Video

Updated: May 16, 2025

Author Spotlight: Investigating Vocal Information Representation in Small Primates and Its Alteration by Psychiatric Disorders Using Noninvasive EEG
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Explainable classification of goat vocalizations using convolutional neural networks.

Stavros Ntalampiras1,2, Gabriele Pesando Gamacchio1

  • 1Department of Computer Science, University of Milan, Milan, Italy.

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|April 1, 2025
PubMed
Summary
This summary is machine-generated.

This study developed a Convolutional Neural Network (CNN) for classifying goat vocalizations, achieving 95.8% accuracy in identifying emotional states. Explainable AI (XAI) methods were used to interpret the model and identify key acoustic features for improved precision livestock farming.

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Area of Science:

  • Animal Science
  • Machine Learning
  • Bioacoustics

Background:

  • Precision livestock farming requires accurate animal and environmental data.
  • Goat vocalizations offer insights into animal welfare and health.
  • Automated analysis of animal sounds is crucial for efficient farm management.

Purpose of the Study:

  • To develop and validate a Convolutional Neural Network (CNN) for classifying goat vocalizations.
  • To enhance model robustness using data augmentation techniques.
  • To interpret the CNN's decision-making process using explainable AI (XAI) for identifying critical acoustic features.

Main Methods:

  • A CNN architecture was designed for goat vocalization classification.
  • Dataset augmentation involved pitch shifting and time stretching.
  • An explainability analysis (XAI) was performed to interpret model decisions.
  • Performance was compared against contrasting approaches.

Main Results:

  • The CNN achieved an average classification rate of 95.8% for discriminating goat emotional states.
  • Data augmentation significantly boosted model robustness and classification accuracy.
  • XAI identified specific time-frequency content crucial for accurate classification.
  • The proposed model demonstrated superiority over alternative methods.

Conclusions:

  • CNNs, enhanced by data augmentation and XAI, are effective for classifying goat vocalizations.
  • XAI provides transparency and identifies key acoustic markers for animal welfare monitoring.
  • The developed interactive scheme offers valuable insights to animal scientists for precision farming.