Cited by 905Music and Audio ProcessingAnimal Vocal Communication and BehaviorSpeech Recognition and Synthesis

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BirdNET: A deep learning solution for avian diversity monitoring

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Kahl, Stefan, Wood, Connor M., Eibl, Maximilian, Klinck, Holger·2021·Ecological Informatics

DOI 10.1016/j.ecoinf.2021.101236

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Abstract

Variation in avian diversity in space and time is commonly used as a metric to assess environmental changes. Conventionally, such data were collected by expert observers, but passively collected acoustic data is rapidly emerging as an alternative survey technique. However, efficiently extracting accurate species richness data from large audio datasets has proven challenging. Recent advances in deep artificial neural networks (DNNs) have transformed the field of machine learning, frequently outperforming traditional signal processing techniques in the domain of acoustic event detection and classification. We developed a DNN, called BirdNET, capable of identifying 984 North American and European bird species by sound. Our task-specific model architecture was derived from the family of residual networks (ResNets), consisted of 157 layers with more than 27 million parameters, and was trained using extensive data pre-processing, augmentation, and mixup. We tested the model against three independent datasets: (a) 22,960 single-species recordings; (b) 286 h of fully annotated soundscape data collected by an array of autonomous recording units in a design analogous to what researchers might use to measure avian diversity in a field setting; and (c) 33,670 h of soundscape data from a single high-quality omnidirectional microphone deployed near four eBird hotspots frequented by expert birders. We found that domain-specific data augmentation is key to build models that are robust against high ambient noise levels and can cope with overlapping vocalizations. Task-specific model designs and training regimes for audio event recognition perform on-par with very complex architectures used in other domains (e.g., object detection in images). We also found that high temporal resolution of input spectrograms (short FFT window length) improves the classification performance for bird sounds. In summary, BirdNET achieved a mean average precision of 0.791 for single-species recordings, a F0.5 score of 0.414 for annotated soundscapes, and an average correlation of 0.251 with hotspot observation across 121 species and 4 years of audio data. By enabling the efficient extraction of the vocalizations of many hundreds of bird species from potentially vast amounts of audio data, BirdNET and similar tools have the potential to add tremendous value to existing and future passively collected audio datasets and may transform the field of avian ecology and conservation.

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Authors: Kahl, S., Wood, C. M., Eibl, M., & Klinck, H.
Affiliations: Cornell University (US)
Chemnitz University of Technology (DE)
Corresponding author: Kahl, Stefan
ORCID: Kahl, Stefan
Wood, Connor M.
Eibl, Maximilian
Klinck, Holger
Venue: Ecological Informatics
Publisher: Elsevier BV
Published: 2021-03
Volume: 61
Pages: 101236
ISSN: 1574-9541 (ISSN-L)
DOI: 10.1016/j.ecoinf.2021.101236
OpenAlex: W3121931845
Cited by: 903
References: 71
Citations by year: 2026: 242 · 2025: 250 · 2024: 197 · 2023: 114 · 2022: 81 · 2021: 19
FWCI: 47.92
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Ecological Informatics (publishedVersion, cc-by)
Funders: Arthur Vining Davis Foundations
European Commission
European Social Fund
Topics: Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
Life Sciences › Agricultural and Biological Sciences › Ecology, Evolution, Behavior and Systematics › Animal Vocal Communication and Behavior
Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
Keywords: BirdNET, sound event detection, passive acoustic monitoring, autonomous recording units, soundscape analysis, ResNet, data augmentation, mixup, ambient noise robustness, bird conservation

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BirdNET: A deep learning solution for avian diversity monitoring

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