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Who is calling? Optimizing source identification from marmoset vocalizations with hierarchical machine learning

Nikhil Phaniraj1,2,3, Kaja Wierucka1,4, Yvonne Zürcher1

  • 1Institute of Evolutionary Anthropology (IEA), University of Zurich, Winterthurerstrasse 190, 8057 Zürich, Switzerland.

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Summary

Researchers developed a machine learning pipeline to accurately identify individual marmosets by their vocalizations. This tool advances the study of social communication and language evolution in these primates.

Keywords:
bioacousticshierarchical classifiermachine learningmarmoset callssource identificationtime series analysis

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Area of Science:

  • Primate communication
  • Bioacoustics
  • Machine learning in animal behavior

Background:

  • Marmosets are crucial models for understanding vocal communication and language evolution due to their social nature and complex vocalizations.
  • Current research on marmoset vocalizations often relies on dyadic interactions or playback studies, limiting the understanding of group-level communication dynamics.
  • Accurate source identification of individual vocalizations is critical for studying complex social communication.

Purpose of the Study:

  • To develop and optimize a machine learning pipeline for accurate source identification of marmoset vocalizations within a group setting.
  • To enable the study of group-level communication dynamics in marmosets.
  • To provide a tool applicable to analyzing vocalizations in other species.

Main Methods:

  • Development of a machine learning pipeline for feature extraction, selection, and supervised classification of marmoset vocalizations.
  • Implementation of a hierarchical machine learning algorithm to first determine the vocalizer's sex, then narrow down the individual, and finally identify the source.
  • Testing the pipeline's accuracy and robustness across different call types and sample sizes, including a dataset with up to 18 marmosets.

Main Results:

  • High precision rates in identifying the source individual (87.21%-94.42% depending on call type).
  • Identification accuracy improved to up to 97.79% when excluding twins from the dataset.
  • Demonstrated robustness of the identification method across varying sample sizes.

Conclusions:

  • The developed machine learning pipeline is a highly effective tool for precise source identification of marmoset vocalizations.
  • This methodology facilitates the investigation of complex, group-level vocal communication in marmosets.
  • The pipeline shows potential for broad application in bioacoustic research across various species.