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Recording Mouse Ultrasonic Vocalizations to Evaluate Social Communication
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A machine learning approach for classifying and quantifying acoustic diversity.

Sara C Keen1,2,3, Karan J Odom3, Michael S Webster2,3

  • 1Center for Conservation Bioacoustics, Cornell Lab of Ornithology, Cornell University, Ithaca, NY, 14850, USA.

Methods in Ecology and Evolution
|December 10, 2021
PubMed
Summary
This summary is machine-generated.

Researchers can now accurately quantify animal vocal repertoire size using a new unsupervised random forest method. This approach automates acoustic diversity analysis, aiding ecological and behavioral studies.

Keywords:
Acoustic diversityacoustic spaceclassificationdata augmentationrandom forestrepertoire sizeunsupervised machine learningvocal signals

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Area of Science:

  • Ethology
  • Bioacoustics
  • Computational Biology

Background:

  • Quantifying vocal repertoire size is crucial for ecological and behavioral studies.
  • Existing methods lack reproducibility and broad applicability for acoustic diversity assessment.

Purpose of the Study:

  • To develop a generalizable, automated method for calculating acoustic diversity and vocal repertoire size.
  • To test the accuracy of unsupervised random forest analysis for classifying acoustic structure.

Main Methods:

  • Utilized an unsupervised random forest framework to analyze acoustic data.
  • Employed cluster analysis and acoustic feature space area estimation to determine repertoire size.
  • Validated the method with natural and synthetic datasets of varying recording quality and known repertoire sizes.

Main Results:

  • Unsupervised random forest analysis accurately classifies acoustic structure.
  • Both cluster analysis and acoustic space area estimation reliably determine repertoire size for small to intermediate repertoires (5-20 elements).
  • Acoustic space area estimation proved more reliable for larger repertoires (20-100 elements).

Conclusions:

  • The unsupervised random forest method provides a generalizable tool for classifying acoustic structure in diverse datasets.
  • This approach enables standardized quantification and comparison of acoustic variation among individuals, populations, or species.
  • Provided R code and examples to facilitate researcher adoption.