ArticlePeerJ2026
Spatial validation of acoustic individual identification models without ground truths: a case study with the cao-vit gibbon population.
Article in PeerJ, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Technological progress has made bioacoustics an important tool for research in the ecology and behaviour of sound producing animals. Using an array of synchronised autonomous recorders, we can localise vocalising animals, and for certain species, computational models can acoustically identify individuals (AIID). Knowing both the precise location and identity of vocalising animals enables a more detailed interpretation of long-term bioacoustic data, but assessing the reliability of AIID models is often difficult, especially for populations that evolve over time. Annotated ground truth labels in test sets are commonly used, but they are often limited in size, and there can be a mismatch with the application data (for instance in case of a change in recording system). Here, we formalise a methodology to evaluate AIID models based on localised predictions, thus bypassing the need for ground truth labels. We demonstrate it on a case study with the critically endangered cao-vit gibbons (
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