Individual identification in acoustic recordings

Elly Knight, Tessa Rhinehart, Devin R. de Zwaan, Matthew J. Weldy, Mark Cartwright, Scott H. Hawley, Jeffery L. Larkin, Damon Lesmeister, Erin Bayne, Justin Kitzes

Research output: Contribution to journalReview articlepeer-review


Recent advances in bioacoustics combined with acoustic individual identification (AIID) could open frontiers for ecological and evolutionary research because traditional methods of identifying individuals are invasive, expensive, labor-intensive, and potentially biased. Despite overwhelming evidence that most taxa have individual acoustic signatures, the application of AIID remains challenging and uncommon. Furthermore, the methods most commonly used for AIID are not compatible with many potential AIID applications. Deep learning in adjacent disciplines suggests opportunities to advance AIID, but such progress is limited by training data. We suggest that broadscale implementation of AIID is achievable, but researchers should prioritize methods that maximize the potential applications of AIID, and develop case studies with easy taxa at smaller spatiotemporal scales before progressing to more difficult scenarios.

Original languageEnglish (US)
JournalTrends in Ecology and Evolution
StateAccepted/In press - 2024

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics


  • acoustic signature
  • bioacoustics
  • communication
  • individual identification
  • passive acoustic monitoring
  • vocalization


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