Individual identification in acoustic recordings

• Published in: Trends in ecology & evolution (Amsterdam) Vol. 39; no. 10; pp. 947 - 960
• Main Authors: Knight, Elly, Rhinehart, Tessa, de Zwaan, Devin R., Weldy, Matthew J., Cartwright, Mark, Hawley, Scott H., Larkin, Jeffery L., Lesmeister, Damon, Bayne, Erin, Kitzes, Justin
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01-10-2024
• Subjects: acoustic signature; Acoustics; Animals; bioacoustics; communication; individual identification; passive acoustic monitoring; vocalization; Vocalization, Animal; acoustic signature; communication; vocalization; bioacoustics; individual identification; passive acoustic monitoring
• ISSN: 0169-5347; 1872-8383; 1872-8383
• DOI: 10.1016/j.tree.2024.05.007

Individual acoustic signatures, if present within animal species, can provide insights into evolution and behavior, and have great potential for differentiating individuals during monitoring and research.Recent advances in bioacoustic technology combined with acoustic individual identification (AIID) have the potential to revolutionize the study of sound-producing animals; however, methods and application of AIID remain in their infancy.Evidence of individual acoustic signatures across taxa and successes in adjacent acoustic disciplines suggest that opportunities exist for developing AIID.Research and development into AIID should combine deep learning methods with the construction and sharing of labeled training datasets, and should focus on recording and classification methods that maximize the potential applications of AIID.Broadscale implementation of AIID should be achievable in the near future and will allow biologists to answer important ecological and evolutionary questions with less bias and fewer negative population effects and resources than the current approaches.
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.
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.