A practical guide for generating unsupervised, spectrogram-based latent space representations of animal vocalizations

Mara Thomas, Frants H. Jensen, Baptiste Averly, Vlad Demartsev, Marta B. Manser, Tim Sainburg, Marie A. Roch, Ariana Strandburg-Peshkin

Research output: Contribution to journalArticlepeer-review

Abstract

Background: The manual detection, analysis and classification of animal vocalizations in acoustic recordings is laborious and requires expert knowledge. Hence, there is a need for objective, generalizable methods that detect underlying patterns in these data, categorize sounds into distinct groups and quantify similarities between them. Among all computational methods that have been proposed to accomplish this, neighbourhood-based dimensionality reduction of spectrograms to produce a latent space representation of calls stands out for its conceptual simplicity and effectiveness. Goal of the study/what was done: Using a dataset of manually annotated meerkat Suricata suricatta vocalizations, we demonstrate how this method can be used to obtain meaningful latent space representations that reflect the established taxonomy of call types. We analyse strengths and weaknesses of the proposed approach, give recommendations for its usage and show application examples, such as the classification of ambiguous calls and the detection of mislabelled calls. What this means: All analyses are accompanied by example code to help researchers realize the potential of this method for the study of animal vocalizations.

Original languageEnglish (US)
Pages (from-to)1567-1581
Number of pages15
JournalJournal of Animal Ecology
Volume91
Issue number8
DOIs
StatePublished - Aug 2022

Keywords

  • UMAP
  • animal sounds
  • animal vocalizations
  • bioacoustics
  • call classification
  • dimensionality reduction
  • spectrogram
  • unsupervised learning

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Animal Science and Zoology

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