Stefan Kahl
Postdoctoral Fellow
How can computers learn to recognize birds from their sounds? As a postdoc within the K. Lisa Yang Center for Conservation Bioacoustics, I am trying to find an answer to this question. My research is mainly focused on the detection and classification of avian sounds using machine learning. Automated observation of avian vocal activity and species diversity can be a transformative tool for ornithologists, conservation biologists, and birdwatchers to assist in long-term monitoring of critical environmental niches.
With a background in computer vision and deep learning, I am mainly focusing on developing new methods to process large data collections of environmental sounds. After completing my master’s degree in Applied Computer Science in 2014, I became a research assistant at the Chemnitz University of Technology, Germany. I was involved in research projects covering human-computer and human-robot interaction, multimodal media retrieval, and mobile application development.
I joined the Yang Center in 2019, continuing my work on a bird sound recognition system I call BirdNET. My goal is to assist experts and participatory scientists in their work of monitoring and protecting our birds by developing a wide range of applications such as smartphone apps, public demonstrators, web interfaces, and robust analysis frameworks.
Education
Ph.D., Chemnitz University of Technology, Germany, 2019
M.Sc., Chemnitz University of Technology, Germany, 2013
B.Sc., Chemnitz University of Technology, Germany, 2011
Recent Publications
2804417
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https://www.birds.cornell.edu/home/wp-content/plugins/zotpress/
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Mann, D., A. Anderson, A. Donner, M. Hall, S. Kahl, and H. Klinck (2025). Continental-scale behavioral response of birds to a total solar eclipse. Scientific Reports 15:10113.
Weidlich-Rau, M., A. K. Navine, P. T. Chaopricha, F. Günther, S. Kahl, T. Wilhelm-Stein, R. C. Mack, H. Reers, A. N. Rice, M. Eibl, P. J. Hart, et al. (2025). Continuous real-time acoustic monitoring of endangered bird species in Hawai'i. Ecological Informatics:103102.
McGinn, K., B. Zuckerberg, G. M. Jones, C. M. Wood, S. Kahl, K. G. Kelly, S. A. Whitmore, H. A. Kramer, J. M. Barry, E. Ng, and M. Z. Peery (2025). Frequent, heterogenous fire supports a forest owl assemblage. Ecological Applications 35:e3080.
Brunk, K. M., J. F. Goldberg, C. Maxwell, M. Z. Peery, G. M. Jones, L. R. Gallagher, H. A. Kramer, A. L. Westerling, J. J. Keane, S. Kahl, and C. M. Wood (2025). Bioregional-scale acoustic monitoring can support fire-prone forest restoration planning. Frontiers in Ecology and the Environment n/a:e2843.
Joly, A., L. Picek, S. Kahl, H. Goëau, L. Adam, C. Botella, M. Servajean, D. Marcos, C. Leblanc, T. Larcher, J. Matas, et al. (2025). LifeCLEF 2025 teaser: challenges on species presence prediction and identification, and individual animal identification. Advances in Information Retrieval (C. Hauff, C. Macdonald, D. Jannach, G. Kazai, F. M. Nardini, F. Pinelli, F. Silvestri and N. Tonellotto, Editors). Springer Nature Switzerland, Cham, pp. 373–381.
Wood, C. M., F. Günther, A. Rex, D. F. Hofstadter, H. Reers, S. Kahl, M. Z. Peery, and H. Klinck (2024). Real-time acoustic monitoring facilitates the proactive management of biological invasions. Biological Invasions. https://doi.org/https://doi.org/10.1007/s10530-024-03426-y
Wood, C. M., and S. Kahl (2024). Guidelines for appropriate use of BirdNET scores and other detector outputs. Journal of Ornithology.
Wood, C. M., J. Socolar, S. Kahl, M. Z. Peery, P. Chaon, K. Kelly, R. A. Koch, S. C. Sawyer, and H. Klinck (2024). A scalable and transferable approach to combining emerging conservation technologies to identify biodiversity change after large disturbances. Journal of Applied Ecology 61:797–808.
Ghani, B., T. Denton, S. Kahl, and H. Klinck (2023). Global birdsong embeddings enable superior transfer learning for bioacoustic classification. Scientific Reports 13:22876.
Sossover, D., K. Burrows, S. Kahl, and C. M. Wood (2023). Using the BirdNET algorithm to identify wolves, coyotes, and potentially their interactions in a large audio dataset. Mammal Research.
Kelly, K. G., C. M. Wood, K. McGinn, H. A. Kramer, S. C. Sawyer, S. Whitmore, D. Reid, S. Kahl, A. Reiss, J. Eiseman, W. Berigan, et al. (2023). Estimating population size for California spotted owls and barred owls across the Sierra Nevada ecosystem with bioacoustics. Ecological Indicators 154:110851.
Kahl, S., T. Denton, H. Klinck, H. Reers, F. Cherutich, H. Glotin, H. Goëau, W.-P. Vellinga, R. Planqué, A. Joly, and K. Yang (2023). Overview of BirdCLEF 2023: Automated Bird Species Identification in Eastern Africa 4.0. Working Notes of CLEF.
McGinn, K., S. Kahl, M. Z. Peery, H. Klinck, and C. M. Wood (2023). Feature embeddings from the BirdNET algorithm provide insights into avian ecology. Ecological Informatics 74:101995.
Brunk, K. M., R. J. Gutiérrez, M. Z. Peery, C. A. Cansler, S. Kahl, and C. M. Wood (2023). Quail on fire: changing fire regimes may benefit mountain quail in fire-adapted forests. Fire Ecology 19:19.
Kahl, S., A. Navine, T. Denton, H. Klinck, P. Hart, H. Glotin, H. Goëau, W.-P. Vellinga, R. Planqué, and A. Joly (2022). Overview of BirdCLEF 2022: Endangered bird species recognition in soundscape recordings. p. 1929.
Joly, A., H. Goëau, S. Kahl, L. Picek, E. Cole, B. Deneu, M. Servajean, A. Durso, H. Glotin, R. Planqué, A. Navine, et al. (2022). Overview of LifeCLEF 2022: an evaluation of Machine-Learning based Species Identification and Species Distribution Prediction.
Wood, C. M., S. Kahl, A. Rahaman, and H. Klinck (2022). The machine learning–powered BirdNET App reduces barriers to global bird research by enabling citizen science participation. PLOS Biology 20:e3001670.
Kahl, S., T. Denton, H. Klinck, H. Glotin, H. Goëau, W. Vellinga, R. Planque, and A. Joly (2021). Overview of BirdCLEF 2021: Bird call identification in soundscape recordings. CLEF 2021.
Wood, C. M., S. Kahl, P. Chaon, M. Z. Peery, and H. Klinck (2021). Survey coverage, recording duration and community composition affect observed species richness in passive acoustic surveys. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210X.13571
Kahl, S., C. M. Wood, M. Eibl, and H. Klinck (2021). BirdNET: A deep learning solution for avian diversity monitoring. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2021.101236
Kahl, S., T. Wilhelm-Stein, H. Klinck, D. Kowerko, and M. Eibl (2018). Recognizing birds from sound--the 2018 BirdCLEF Baseline System. Computer Vision and Pattern Recognition. https://doi.org/arXiv:1804.07177v1
Kahl, S., S. Wilhelm-Stein, H. Hussein, H. Klinck, D. Kowerko, M. Ritter, and M. Eibl (2017). Large-Scale Bird Sound Classification using Convolutional Neural Networks. LifeCLEF 2017 Dublin, Ireland, 12-13 September 2017.
Brunk, K. B., H. A. Kramer, S. Kahl, M. Z. Peery, and C. M. Wood (no date). [In press] Ecosystem-scale assessment of complementary indicator species reveals an expansive but spatially variable conservation umbrella. Conservation Biology.
