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Machine Learning Algorithms for Automatic Classification of Marmoset Vocalizations

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  • Hjalmar K Turesson
  • Sidarta Ribeiro
  • Danillo R Pereira
  • João P Papa
  • Victor Hugo C de Albuquerque

Abstract

Automatic classification of vocalization type could potentially become a useful tool for acoustic the monitoring of captive colonies of highly vocal primates. However, for classification to be useful in practice, a reliable algorithm that can be successfully trained on small datasets is necessary. In this work, we consider seven different classification algorithms with the goal of finding a robust classifier that can be successfully trained on small datasets. We found good classification performance (accuracy > 0.83 and F1-score > 0.84) using the Optimum Path Forest classifier. Dataset and algorithms are made publicly available.

Suggested Citation

  • Hjalmar K Turesson & Sidarta Ribeiro & Danillo R Pereira & João P Papa & Victor Hugo C de Albuquerque, 2016. "Machine Learning Algorithms for Automatic Classification of Marmoset Vocalizations," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-14, September.
    • Handle: RePEc:plo:pone00:0163041
    DOI: 10.1371/journal.pone.0163041
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    Cited by:

    1. Sandhya Sharma & Kazuhiko Sato & Bishnu Prasad Gautam, 2023. "A Methodological Literature Review of Acoustic Wildlife Monitoring Using Artificial Intelligence Tools and Techniques," Sustainability, MDPI, vol. 15(9), pages 1-20, April.

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