The use of BirdNET embeddings as a fast solution to find novel sound classes in audio recordings
Allen-Ankins, Slade, Hoefer, Sebastian, Bartholomew, Jacopo, Brodie, Sheryn, and Schwarzkopf, Lin (2024) The use of BirdNET embeddings as a fast solution to find novel sound classes in audio recordings. Frontiers in Ecology and Evolution, 12. 1409407.
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Abstract
Passive acoustic monitoring has emerged as a useful technique for monitoring vocal species and contributing to biodiversity monitoring goals. However, finding target sounds for species without pre-existing recognisers still proves challenging. Here, we demonstrate how the embeddings from the large acoustic model BirdNET can be used to quickly and easily find new sound classes outside the original model’s training set. We outline the general workflow, and present three case studies covering a range of ecological use cases that we believe are common requirements in research and management: monitoring invasive species, generating species lists, and detecting threatened species. In all cases, a minimal amount of target class examples and validation effort was required to obtain results applicable to the desired application. The demonstrated success of this method across different datasets and different taxonomic groups suggests a wide applicability of BirdNET embeddings for finding novel sound classes. We anticipate this method will allow easy and rapid detection of sound classes for which no current recognisers exist, contributing to both monitoring and conservation goals.
| Item ID: | 87348 |
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| Item Type: | Article (Research - C1) |
| ISSN: | 2296-701X |
| Keywords: | acoustic recognition, bioacoustics, biodiversity, conservation, deep learning, passive acoustic monitoring |
| Copyright Information: | © 2025 Allen-Ankins, Hoefer, Bartholomew, Brodie and Schwarzkopf. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| Date Deposited: | 13 Nov 2025 04:25 |
| FoR Codes: | 41 ENVIRONMENTAL SCIENCES > 4102 Ecological applications > 410299 Ecological applications not elsewhere classified @ 40% 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460299 Artificial intelligence not elsewhere classified @ 60% |
| SEO Codes: | 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220402 Applied computing @ 50% 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280102 Expanding knowledge in the biological sciences @ 50% |
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