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Study on a Pig Vocalization Classification Method Based on Multi-Feature Fusion.

Yuting Hou1,2, Qifeng Li1,3, Zuchao Wang2

  • 1Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.

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|January 23, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for classifying pig vocalizations using multi-feature fusion and a genetic algorithm-optimized neural network. The approach achieves high accuracy in recognizing grunts, squeals, and coughs, aiding in animal welfare monitoring.

Keywords:
classification recognitionmulti-feature fusionpig vocalizationprincipal component analysis

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Area of Science:

  • Animal Science
  • Bioacoustics
  • Machine Learning

Background:

  • Accurate classification of pig vocalizations is crucial for monitoring animal welfare and health in large-scale breeding operations.
  • Existing methods may lack the precision required for nuanced interpretation of pig vocal signals.

Purpose of the Study:

  • To develop and validate a robust pig vocalization classification method.
  • To enhance recognition accuracy by fusing multiple acoustic features and employing advanced machine learning algorithms.

Main Methods:

  • Extracted short-time energy, frequency centroid, formant frequency, and Mel frequency cepstral coefficients as fusion features.
  • Applied Principal Component Analysis (PCA) for feature improvement.
  • Constructed a Backpropagation (BP) neural network model optimized with a genetic algorithm.

Main Results:

  • Achieved an average recognition accuracy of 93.2% for pig grunting, squealing, and coughing.
  • Obtained average recognition precision of 92.9% and average recall of 92.8%.
  • Demonstrated superior performance in distinguishing between different types of pig vocalizations.

Conclusions:

  • The proposed multi-feature fusion method significantly improves pig vocalization classification accuracy.
  • The genetic algorithm-optimized BP neural network provides a reliable tool for automatic recognition of pig vocal signals.
  • This method offers valuable insights for pig vocalization information feedback and automated monitoring systems.