Individual identification in acoustic recordings

Authors

Elly Knight, University of Alberta
Tessa Rhinehart, University of Pittsburgh
Devin R. de Zwaan, Mount Allison University
Matthew J. Weldy, Oregon State University
Mark Cartwright, Ying Wu College of Computing
Scott H. Hawley, Belmont University
Jeffery L. Larkin, Indiana University of Pennsylvania
Damon Lesmeister, Oregon State University
Erin Bayne, University of Alberta
Justin Kitzes, University of Pittsburgh

Document Type

Article

Publication Date

10-1-2024

Abstract

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.

Identifier

85195557622 (Scopus)

Publication Title

Trends in Ecology and Evolution

External Full Text Location

https://doi.org/10.1016/j.tree.2024.05.007

ISSN

01695347

PubMed ID

38862357

First Page

947

Last Page

960

Issue

10

Volume

39

Recommended Citation

Knight, Elly; Rhinehart, Tessa; de Zwaan, Devin R.; Weldy, Matthew J.; Cartwright, Mark; Hawley, Scott H.; Larkin, Jeffery L.; Lesmeister, Damon; Bayne, Erin; and Kitzes, Justin, "Individual identification in acoustic recordings" (2024). Faculty Publications. 176.
https://digitalcommons.njit.edu/fac_pubs/176