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BirdNET: A deep learning solution for avian diversity monitoring
Open accessAbstract
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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“More recently, artificial intelligence deep learning models, such as Convolutional Neural Networks (CNN), have been developed to identify birds, bats or amphibians36–40.”
From Soundscapes and deep learning enable tracking biodiversity recovery in tropical forests (2023) · cites this paper as [40]
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“Models pre-trained on ImageNet are configured to extract a variety of generic image features that can be useful for spectrogram recognition40.”
From Soundscapes and deep learning enable tracking biodiversity recovery in tropical forests (2023) · cites this paper as [40]
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“While ML has been extensively applied to camera trap images, its application to long-term PAM datasets is still in its infancy and the first DL-based studies are only starting to appear”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [61]
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273
Distilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204
Proceedings of IEEE International Conference on Computer Vision
(1995) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466933 · Cited by 13,117
Zagoruyko, S., & Komodakis, N. (2016) · Procedings of the British Machine Vision Conference 2016 · doi:10.5244/c.30.87 · Cited by 7,244
mixup: Beyond Empirical Risk Minimization
Zhang, H., Cisse, M., Dauphin, Y. N., & Lopez-Paz, D. (2017) · arXiv · doi:10.48550/arxiv.1710.09412 · Cited by 5,831
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
Park, D. S., Chan, W., Zhang, Y., Chiu, C.-C., Zoph, B., Cubuk, E. D., & Le, Q. V. (2019) · Interspeech 2019 · doi:10.21437/interspeech.2019-2680 · Cited by 3,558
Audio Set: An ontology and human-labeled dataset for audio events
Gemmeke, J. F., Ellis, D. P. W., Freedman, D., Jansen, A., Lawrence, W., Moore, R. C., Plakal, M., & Ritter, M. (2017) · 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2017.7952261 · Cited by 3,134
Research works printed but not linked: 1
- Kiyokawa, 2019, Sound event detection with ResNet and self-mask module for DCASE 2019 task 4, Tech. Rep.
Cited by
Sorted by citations
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Self-citationComputational bioacoustics with deep learning: a review and roadmap
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Borowiec, M. L., Dikow, R. B., Frandsen, P. B., McKeeken, A. J., Valentini, G., & White, A. E. (2022) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13901 · Cited by 295
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Global birdsong embeddings enable superior transfer learning for bioacoustic classification
Ghani, B., Denton, T., Kahl, S., & Klinck, H. (2023) · Scientific Reports · doi:10.1038/s41598-023-49989-z · Cited by 167
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Sounding the Call for a Global Library of Underwater Biological Sounds
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Xie, J., Zhong, Y., Zhang, J., Liu, S., Ding, C., & Triantafyllopoulos, A. (2022) · Ecological Informatics · doi:10.1016/j.ecoinf.2022.101927 · Cited by 120
The potential for AI to revolutionize conservation: a horizon scan
Reynolds, S. A., Beery, S., Burgess, N. D., Burgman, M. A., Butchart, S. H. M., Cooke, S. J., Coomes, D. A., Danielsen, F., Minin, E. D., Durán, A. P., et al. (2024) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2024.11.013 · Cited by 139
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Sethi, S. S., Bick, A., Ewers, R. M., Klinck, H., Ramesh, V., Tuanmu, M.-N., & Coomes, D. A. (2023) · Nature Ecology & Evolution · doi:10.1038/s41559-023-02148-z · Cited by 95
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Müller, J., Mitesser, O., Schaefer, H. M., Seibold, S., Busse, A., Kriegel, P., Rabl, D., Gelis, R., Arteaga, A., Freile, J., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41693-w · Cited by 89
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Turchet, L., Lagrange, M., Rottondi, C., Fazekas, G., Peters, N., Østergaard, J., Font, F., Bäckström, T., & Fischione, C. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3253602 · Cited by 100
Wood, C. M., Kahl, S., Chaon, P., Peery, M. Z., & Klinck, H. (2021) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13571 · Cited by 84
Self-citationWood, C. M., Kahl, S., Rahaman, A., & Klinck, H. (2022) · PLoS Biology · doi:10.1371/journal.pbio.3001670 · Cited by 76
Self-citationFunosas, D., Barbaro, L., Schillé, L., Elger, A., Castagneyrol, B., & Cauchoix, M. (2024) · Ecological Indicators · doi:10.1016/j.ecolind.2024.112146 · Cited by 74
Cole, J. S., Michel, N. L., Emerson, S. A., & Siegel, R. B. (2022) · Ornithological Applications · doi:10.1093/ornithapp/duac003 · Cited by 71
Lauha, P., Somervuo, P. J., Lehikoinen, P., Geres, L. S., Richter, T., Seibold, S., & Ovaskainen, O. (2022) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.14003 · Cited by 78
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Nieto-Mora, D. A., Rodríguez‐Buriticá, S., Marín, P. A. R., Martínez-Vargaz, J. D., & Isaza, C. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e20275 · Cited by 76
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Nolasco, I., Singh, S., Morfi, V., Lostanlen, V., Strandburg‐Peshkin, A., Vidaña-Vila, E., Gill, L., Pamuła, H., Whitehead, H., Kiskin, I., et al. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102258 · Cited by 67
Manzano‐Rubio, R., Bota, G., Brotons, L., Soto-Largo, E., & Pérez‐Granados, C. (2022) · Ecological Informatics · doi:10.1016/j.ecoinf.2022.101910 · Cited by 61
Kershenbaum, A., Akçay, Ç., Saheer, L. B., Barnhill, A., Best, P., Cauzinille, J., Clink, D. J., Dassow, A., Dufourq, E., Growcott, J., et al. (2024) · Biological reviews/Biological reviews of the Cambridge Philosophical Society · doi:10.1111/brv.13155 · Cited by 72
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McGinn, K. A., Kahl, S., Peery, M. Z., Klinck, H., & Wood, C. M. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.101995 · Cited by 60
Self-citationBota, G., Manzano‐Rubio, R., Catalán, L., Gómez‐Catasús, J., & Pérez‐Granados, C. (2023) · Sensors · doi:10.3390/s23167176 · Cited by 57
Quinn, C. A., Burns, P., Gill, G., Baligar, S., Snyder, R. L., Salas, L. A., Goetz, S. J., & Clark, M. L. (2022) · Ecological Indicators · doi:10.1016/j.ecolind.2022.108831 · Cited by 64
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Haupert, S., Sèbe, F., & Sueur, J. (2022) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.14020 · Cited by 54
Napier, T., Ahn, E., Allen‐Ankins, S., Schwarzkopf, L., & Lee, I. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124220 · Cited by 55
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Sethi, S. S., Bick, I. A., Chen, M., Crouzeilles, R., Hillier, B. V., Lawson, J., Lee, C.-Y., Liu, S.-H., Parruco, C. H. D. F., Rosten, C. M., et al. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2315933121 · Cited by 50
Sun, Y., Midori Maeda, T., Solís-Lemus, C., Pimentel-Alarcón, D., & Buřivalová, Z. (2022) · Ecological Indicators · doi:10.1016/j.ecolind.2022.109621 · Cited by 49
Kath, H., Serafini, P. P., Campos, I. B., Gouvêa, T. S., & Sonntag, D. (2024) · Ecological Informatics · doi:10.1016/j.ecoinf.2024.102710 · Cited by 58
An Efficient Model for a Vast Number of Bird Species Identification Based on Acoustic Features
Wang, H., Xu, Y., Yan, Y., Lin, Y., & Ran, J. (2022) · Animals · doi:10.3390/ani12182434 · Cited by 51
Unsupervised classification to improve the quality of a bird song recording dataset
Michaud, F., Sueur, J., Cesne, M. L., & Haupert, S. (2022) · Ecological Informatics · doi:10.1016/j.ecoinf.2022.101952 · Cited by 54
Folliot, A., Haupert, S., Ducrettet, M., Sèbe, F., & Sueur, J. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.155883 · Cited by 56
Algorithms going wild – A review of machine learning techniques for terrestrial ecology
Cipriano, C., Noce, S., Mereu, S., & Santini, M. (2025) · Ecological Modelling · doi:10.1016/j.ecolmodel.2025.111164 · Cited by 50
Improving deep learning acoustic classifiers with contextual information for wildlife monitoring
Jeantet, L., & Dufourq, E. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102256 · Cited by 57
Wood, C. M., Kahl, S., Barnes, S., Horne, R. V., & Brown, C. (2023) · Bioacoustics · doi:10.1080/09524622.2023.2211544 · Cited by 45
Self-citationExploring the relationship between the soundscape and the environment: A systematic review
Turlington, K., Suárez‐Castro, A. F., Teixeira, D., Linke, S., & Sheldon, F. (2024) · Ecological Indicators · doi:10.1016/j.ecolind.2024.112388 · Cited by 43
Overcoming biodiversity blindness: Secondary data in primary citizen science observations
Pernat, N., Canavan, S., Golivets, M., Hillaert, J., Itescu, Y., Jarić, I., Mann, H. M. R., Pipek, P., Preda, C., Richardson, D. M., et al. (2024) · Ecological Solutions and Evidence · doi:10.1002/2688-8319.12295 · Cited by 41
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Oestreich, W. K., Oliver, R. Y., Chapman, M., Go, M. C., & McKenna, M. F. (2024) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2024.06.007 · Cited by 43
Comparing N-mixture models and GLMMs for relative abundance estimation in a citizen science dataset
Goldstein, B. R., & de Valpine, P. (2022) · Scientific Reports · doi:10.1038/s41598-022-16368-z · Cited by 39
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van Merriënboer, B., Hamer, J., Dumoulin, V., Triantafillou, E., & Denton, T. (2024) · Frontiers in Bird Science · doi:10.3389/fbirs.2024.1369756 · Cited by 51
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Teixeira, D., Roe, P., van Rensburg, B. J., Linke, S., McDonald, P. G., Tucker, D., & Fuller, S. (2024) · Conservation Science and Practice · doi:10.1111/csp2.13132 · Cited by 38
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Gorleri, F. C., Jordan, E. A., Roesler, I., Monteleone, D. L., & Areta, J. I. (2022) · Ibis · doi:10.1111/ibi.13137 · Cited by 38
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Kim, S. S. Y., Watkins, E. A., Russakovsky, O., Fong, R., & Monroy-Hernández, A. (2023) · 2023 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3593013.3593978 · Cited by 37
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Bicudo, T., Llusia, D., Anciães, M., & Gil, D. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102241 · Cited by 39
Wood, C. M., Socolar, J. B., Kahl, S., Peery, M. Z., Chaon, P., Kelly, K. G., Koch, R. A., Sawyer, S. C., & Klinck, H. (2024) · Journal of Applied Ecology · doi:10.1111/1365-2664.14579 · Cited by 40
Self-citationSinger, D., Hagge, J., Kamp, J., Hondong, H., & Schuldt, A. (2024) · Remote Sensing in Ecology and Conservation · doi:10.1002/rse2.385 · Cited by 36
Wood, C. M., Cruz, A. B., & Kahl, S. (2023) · American Journal of Primatology · doi:10.1002/ajp.23507 · Cited by 34
Self-citationA Novel Bird Sound Recognition Method Based on Multifeature Fusion and a Transformer Encoder
Zhang, S., Gao, Y., Cai, J., Yang, H., Zhao, Q., & Pan, F. (2023) · Sensors · doi:10.3390/s23198099 · Cited by 38
Clark, M. L., Salas, L. A., Baligar, S., Quinn, C. A., Snyder, R. L., Leland, D., Schackwitz, W., Goetz, S. J., & Newsam, S. D. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102065 · Cited by 34
Applications of machine learning to identify and characterize the sounds produced by fish
Barroso, V. R., Xavier, F. C., & Ferreira, C. E. L. (2023) · ICES Journal of Marine Science · doi:10.1093/icesjms/fsad126 · Cited by 34
Kelly, K. G., Wood, C. M., McGinn, K. A., Kramer, H. A., Sawyer, S. C., Whitmore, S. A., Reid, D. S., Kahl, S., Reiss, A., Eiseman, J. P., et al. (2023) · Ecological Indicators · doi:10.1016/j.ecolind.2023.110851 · Cited by 34
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Höchst, J., Bellafkir, H., Lampe, P., Vogelbacher, M., Mühling, M., Schneider, D. M., Lindner, K., Rösner, S., Schabo, D. G., Farwig, N., et al. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-17436-0_6 · Cited by 34
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Rasmussen, J. H., Stowell, D., & Briefer, E. F. (2024) · Science · doi:10.1126/science.adh2716 · Cited by 33
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Sethi, S. S., Ewers, R. M., Jones, N. S., Sleutel, J., Shabrani, A., Zulkifli, N., & Picinali, L. (2022) · Oikos · doi:10.1111/oik.08525 · Cited by 34
Symes, L. B., Kittelberger, K. D., Stone, S. M., Holmes, R. T., Jones, J. S., Ruvalcaba, I. P. C., Webster, M. S., & Ayres, M. P. (2022) · Ecology and Evolution · doi:10.1002/ece3.8797 · Cited by 29
AMResNet: An automatic recognition model of bird sounds in real environment
Xiao, H., Liu, D., Chen, K., & Zhu, M. (2022) · Applied Acoustics · doi:10.1016/j.apacoust.2022.109121 · Cited by 33
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Hagiwara, M. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095642 · Cited by 66
Transound: Hyper-head attention transformer for birds sound recognition
Tang, Q., Xu, L., Zheng, B., & He, C. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102001 · Cited by 33
Ware, L., Mahon, C. L., McLeod, L. J. T., & Jetté, J.-F. (2023) · Canadian Journal of Zoology · doi:10.1139/cjz-2023-0044 · Cited by 45
Towards interpretable learned representations for ecoacoustics using variational auto-encoding
Gibb, K., Eldridge, A., Sandom, C. J., & Simpson, I. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102449 · Cited by 33
Using tropical reef, bird and unrelated sounds for superior transfer learning in marine bioacoustics
Williams, B., van Merriënboer, B., Dumoulin, V., Hamer, J., Fleishman, A. B., McKown, M., Munger, J. E., Rice, A. N., Lillis, A., White, C., et al. (2025) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2024.0280 · Cited by 32
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Segura-García, J., Sturley, S., Arevalillo‐Herráez, M., Calero, J. M. A., Felici-Castell, S., & Navarro, E. A. (2024) · Sensors · doi:10.3390/s24113687 · Cited by 31
Truong, M.-X., & van der Wal, R. (2024) · BioScience · doi:10.1093/biosci/biae077 · Cited by 35
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Heinrich, R., Rauch, L., Sick, B., & Scholz, C. (2025) · Ecological Informatics · doi:10.1016/j.ecoinf.2025.103081 · Cited by 34
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Bota, G., Manzano‐Rubio, R., Fanlo, H., Franch, N., Brotons, L., Villero, D., Devisscher, S., Pavesi, A., Cavaletti, E., & Pérez‐Granados, C. (2024) · Biological Invasions · doi:10.1007/s10530-023-03244-8 · Cited by 30
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Zeuss, D., Bald, L., Gottwald, J., Becker, M., Bellafkir, H., Bendix, J., Bengel, P. T., Beumer, L. T., Brandl, R., Brändle, M., et al. (2023) · Global Change Biology · doi:10.1111/gcb.17056 · Cited by 29
Lakdari, M. W., Ahmad, A. H., Sethi, S. S., Bohn, G. A., & Clink, D. J. (2024) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102457 · Cited by 32
Zacharias, S., Loescher, H. W., Bogena, H., Kiese, R., Schrön, M., Attinger, S., Blume, T., Borchardt, D., Borg, E., Bumberger, J., et al. (2024) · Earth s Future · doi:10.1029/2024ef004510 · Cited by 39
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Brunk, K. M., Gutiérrez, R. J., Peery, M. Z., Cansler, C. A., Kahl, S., & Wood, C. M. (2023) · Fire Ecology · doi:10.1186/s42408-023-00180-9 · Cited by 28
Self-citationDuarte, A., Weldy, M. J., Lesmeister, D. B., Ruff, Z. J., Jenkins, J. M. A., Valente, J. J., & Betts, M. G. (2024) · Ecological Indicators · doi:10.1016/j.ecolind.2024.112016 · Cited by 28
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Shaw, T., Scherer‐Lorenzen, M., & Müller, S. (2024) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2024.1387879 · Cited by 29
Jäckel, D., Mortega, K. G., Sturm, U., Brockmeyer, U., Khorramshahi, O., & Voigt‐Heucke, S. L. (2021) · PLoS ONE · doi:10.1371/journal.pone.0253763 · Cited by 29
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Wang, Q., Song, Y., Du, Y., Yang, Z., Cui, P. L., & Luo, B. (2024) · Ecological Informatics · doi:10.1016/j.ecoinf.2024.102538 · Cited by 25
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Joly, A., Goëau, H., Kahl, S., Picek, L., Lorieul, T., Cole, E., Deneu, B., Servajean, M., Durso, A. M., Bolon, I., et al. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-85251-1_24 · Cited by 23
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Metadata
- Authors
- Kahl, S., Wood, C. M., Eibl, M., & Klinck, H.
- Affiliations
- Cornell University (US)
- Chemnitz University of Technology (DE)
- Corresponding author
- Kahl, Stefan
- Venue
- Ecological Informatics
- Publisher
- Elsevier BV
- Published
- 2021-03
- Volume
- 61
- Pages
- 101236
- ISSN
- 1574-9541 (ISSN-L)
- 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
- 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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