ArcticSoundsNET: BirdNET embeddings facilitate improved bioacoustic classification of Arctic species
In recent years, deep learning has become a popular solution for processing large ecological monitoring datasets. This rise in use has resulted in global classification models for a variety of data types and taxa, such as BirdNET, which classifies vocalizations of more than 6000 avian species from a...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-12-01
|
| Series: | Ecological Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125002791 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849233617766580224 |
|---|---|
| author | Morgan A. Ziegenhorn Richard B. Lanctot Stephen C. Brown Miles Brengle Shiloh Schulte Sarah T. Saalfeld Christopher J. Latty Paul A. Smith Nicolas Lecomte |
| author_facet | Morgan A. Ziegenhorn Richard B. Lanctot Stephen C. Brown Miles Brengle Shiloh Schulte Sarah T. Saalfeld Christopher J. Latty Paul A. Smith Nicolas Lecomte |
| author_sort | Morgan A. Ziegenhorn |
| collection | DOAJ |
| description | In recent years, deep learning has become a popular solution for processing large ecological monitoring datasets. This rise in use has resulted in global classification models for a variety of data types and taxa, such as BirdNET, which classifies vocalizations of more than 6000 avian species from acoustic data. These global models can be useful pre-trained models for transfer learning, allowing researchers to more easily develop classifiers specialized to their datasets. However, the development of such models hinges on the availability of comprehensive, high-quality training data, which can be difficult to acquire, produce, and use. We present a novel pipeline for creating training data from a large and unlabeled dataset with minimal human oversight. We used this pipeline and BirdNET as our base model to develop a transfer-learning-based model, ArcticSoundsNET, using acoustic monitoring data from 205 sites across Alaska's Arctic Coastal Plain. We compared performance of ArcticSoundsNET with that of BirdNET to evaluate the effectiveness of our pipeline and success of the new model. We found that the ability of ArcticSoundsNET to detect and classify avian vocalizations in our data greatly exceeded that of BirdNET (AUC ROC = 0.888 for ArcticSoundsNET, AUC ROC = 0.593 for BirdNET). Importantly, our method for developing a training dataset is widely applicable for ecologists who do not have large amounts of labeled data, facilitating the creation of task-specific classification models. Developing such models is an essential step in using large acoustic datasets to support ecological conservation of critical species and habitats. |
| format | Article |
| id | doaj-art-dc4df117c39d491cb2a24eb48e7132cc |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-dc4df117c39d491cb2a24eb48e7132cc2025-08-20T05:05:28ZengElsevierEcological Informatics1574-95412025-12-019010327010.1016/j.ecoinf.2025.103270ArcticSoundsNET: BirdNET embeddings facilitate improved bioacoustic classification of Arctic speciesMorgan A. Ziegenhorn0Richard B. Lanctot1Stephen C. Brown2Miles Brengle3Shiloh Schulte4Sarah T. Saalfeld5Christopher J. Latty6Paul A. Smith7Nicolas Lecomte8Department of Biology and Centre d'Études Nordiques, University of Moncton, Moncton, New Brunswick E1A 3E9, Canada; Corresponding author.U.S. Fish and Wildlife Service, Migratory Bird Management, Anchorage, AK 99503, USAManomet, Inc., Manomet, MA 02360, USAIndependent ResearcherManomet, Inc., Manomet, MA 02360, USAU.S. Fish and Wildlife Service, Migratory Bird Management, Anchorage, AK 99503, USAU.S. Fish and Wildlife Service, Northern Alaska Fish and Wildlife Field Office, Fairbanks, AK 99701-6237, USANational Wildlife Research Centre, Carleton University, Ottawa, Ontario K1A 0H3, CanadaDepartment of Biology and Centre d'Études Nordiques, University of Moncton, Moncton, New Brunswick E1A 3E9, CanadaIn recent years, deep learning has become a popular solution for processing large ecological monitoring datasets. This rise in use has resulted in global classification models for a variety of data types and taxa, such as BirdNET, which classifies vocalizations of more than 6000 avian species from acoustic data. These global models can be useful pre-trained models for transfer learning, allowing researchers to more easily develop classifiers specialized to their datasets. However, the development of such models hinges on the availability of comprehensive, high-quality training data, which can be difficult to acquire, produce, and use. We present a novel pipeline for creating training data from a large and unlabeled dataset with minimal human oversight. We used this pipeline and BirdNET as our base model to develop a transfer-learning-based model, ArcticSoundsNET, using acoustic monitoring data from 205 sites across Alaska's Arctic Coastal Plain. We compared performance of ArcticSoundsNET with that of BirdNET to evaluate the effectiveness of our pipeline and success of the new model. We found that the ability of ArcticSoundsNET to detect and classify avian vocalizations in our data greatly exceeded that of BirdNET (AUC ROC = 0.888 for ArcticSoundsNET, AUC ROC = 0.593 for BirdNET). Importantly, our method for developing a training dataset is widely applicable for ecologists who do not have large amounts of labeled data, facilitating the creation of task-specific classification models. Developing such models is an essential step in using large acoustic datasets to support ecological conservation of critical species and habitats.http://www.sciencedirect.com/science/article/pii/S1574954125002791Deep learningTransfer learningArcticBirdsBirdNETPassive acoustic monitoring |
| spellingShingle | Morgan A. Ziegenhorn Richard B. Lanctot Stephen C. Brown Miles Brengle Shiloh Schulte Sarah T. Saalfeld Christopher J. Latty Paul A. Smith Nicolas Lecomte ArcticSoundsNET: BirdNET embeddings facilitate improved bioacoustic classification of Arctic species Ecological Informatics Deep learning Transfer learning Arctic Birds BirdNET Passive acoustic monitoring |
| title | ArcticSoundsNET: BirdNET embeddings facilitate improved bioacoustic classification of Arctic species |
| title_full | ArcticSoundsNET: BirdNET embeddings facilitate improved bioacoustic classification of Arctic species |
| title_fullStr | ArcticSoundsNET: BirdNET embeddings facilitate improved bioacoustic classification of Arctic species |
| title_full_unstemmed | ArcticSoundsNET: BirdNET embeddings facilitate improved bioacoustic classification of Arctic species |
| title_short | ArcticSoundsNET: BirdNET embeddings facilitate improved bioacoustic classification of Arctic species |
| title_sort | arcticsoundsnet birdnet embeddings facilitate improved bioacoustic classification of arctic species |
| topic | Deep learning Transfer learning Arctic Birds BirdNET Passive acoustic monitoring |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125002791 |
| work_keys_str_mv | AT morganaziegenhorn arcticsoundsnetbirdnetembeddingsfacilitateimprovedbioacousticclassificationofarcticspecies AT richardblanctot arcticsoundsnetbirdnetembeddingsfacilitateimprovedbioacousticclassificationofarcticspecies AT stephencbrown arcticsoundsnetbirdnetembeddingsfacilitateimprovedbioacousticclassificationofarcticspecies AT milesbrengle arcticsoundsnetbirdnetembeddingsfacilitateimprovedbioacousticclassificationofarcticspecies AT shilohschulte arcticsoundsnetbirdnetembeddingsfacilitateimprovedbioacousticclassificationofarcticspecies AT sarahtsaalfeld arcticsoundsnetbirdnetembeddingsfacilitateimprovedbioacousticclassificationofarcticspecies AT christopherjlatty arcticsoundsnetbirdnetembeddingsfacilitateimprovedbioacousticclassificationofarcticspecies AT paulasmith arcticsoundsnetbirdnetembeddingsfacilitateimprovedbioacousticclassificationofarcticspecies AT nicolaslecomte arcticsoundsnetbirdnetembeddingsfacilitateimprovedbioacousticclassificationofarcticspecies |