OpenSoundscape: An open‐source bioacoustics analysis package for Python
Open accessAbstract
Landscape‐scale bioacoustic projects have become a popular approach to biodiversity monitoring. Combining passive acoustic monitoring recordings and automated detection provides an effective means of monitoring sound‐producing species' occupancy and phenology and can lend insight into unobserved behaviours and patterns. The availability of low‐cost recording hardware has lowered barriers to large‐scale data collection, but technological barriers in data analysis remain a bottleneck for extracting biological insight from bioacoustic datasets. We provide a robust and open‐source Python toolkit for detecting and localizing biological sounds in acoustic data. OpenSoundscape provides access to automated acoustic detection, classification and localization methods through a simple and easy‐to‐use set of tools. Extensive documentation and tutorials provide step‐by‐step instructions and examples of end‐to‐end analysis of bioacoustic data. Here, we describe the functionality of this package and provide concise examples of bioacoustic analyses with OpenSoundscape. By providing an interface for bioacoustic data and methods, we hope this package will lead to increased adoption of bioacoustics methods and ultimately to enhanced insights for ecology and conservation.
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The open-source software boom has democratized access to bioacoustic methods; Python libraries like OpenSoundscape, librosa, Parselmouth, and scikit-maad let anyone build modular detection and classification pipelines.
“Python libraries like OpenSoundscape, librosa, Parselmouth and scikit-maad now let anyone build modular detection and classification pipelines”
From Listening forward: emerging roles of bioacoustics in ecology, evolution, and conservation (2026) · cites this paper as Lapp et al. 2023
Transfer learning with embeddings from massive avian models like BirdNET or platforms such as OpenSoundscape is surprisingly general: with just a handful of labeled examples per class, researchers can fine-tune these systems for entirely new habitats, species, or whole taxonomic groups.
“Embeddings from massive avian models like BirdNET or platforms such as OpenSoundscape have turned out to be surprisingly general: with just a handful of labeled examples per class, researchers can fine-tune these systems for entirely new habitats, species, or even whole taxonomic groups”
From Listening forward: emerging roles of bioacoustics in ecology, evolution, and conservation (2026) · cites this paper as Lapp et al. 2023
Cites
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
Matplotlib: a 2D graphics environment
Hunter, J. D. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.55 · Cited by 39,249 · see citing works
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019) · arXiv · doi:10.48550/arxiv.1912.01703 · Cited by 31,473 · see citing works
Data Structures for Statistical Computing in Python
McKinney, W. (2010) · Proceedings of the Python in Science Conferences · doi:10.25080/majora-92bf1922-00a · Cited by 11,511 · see citing works
The NumPy Array: A Structure for Efficient Numerical Computation
van der Walt, S., Colbert, S. C., & Varoquaux, G. P. (2011) · Computing in Science & Engineering · doi:10.1109/mcse.2011.37 · Cited by 10,445 · see citing works
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01228-7 · Cited by 6,909 · see citing works
librosa: Audio and Music Signal Analysis in Python
McFee, B., Raffel, C., Liang, D., Ellis, D. P. W., McVicar, M., Battenberg, E., & Nieto, O. (2015) · Proceedings of the Python in Science Conferences · doi:10.25080/majora-7b98e3ed-003 · Cited by 3,099 · see citing works
Lam, S. K., Pitrou, A., & Seibert, S. (2015) · doi:10.1145/2833157.2833162 · Cited by 1,522 · see citing works
PyWavelets: A Python package for wavelet analysis
Lee, G. R., Gommers, R., Waselewski, F., Wohlfahrt, K., & O'Leary, A. (2019) · The Journal of Open Source Software · doi:10.21105/joss.01237 · Cited by 780 · see citing works
Computational bioacoustics with deep learning: a review and roadmap
Stowell, D. (2022) · PeerJ · doi:10.7717/peerj.13152 · Cited by 471 · see citing works
Torchvision the machine-vision package of torch
Marcel, S., & Rodriguez, Y. (2010) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1873951.1874254 · Cited by 430 · see citing works
Gillespie, D. M., Mellinger, D. K., Gordon, J. C. D., McLaren, D., Redmond, P. H., McHugh, R., Trinder, P. W., Deng, X.-Y., & Thode, A. M. (2009) · The Journal of the Acoustical Society of America · doi:10.1121/1.4808713 · Cited by 232 · see citing works
Deep Machine Learning Techniques for the Detection and Classification of Sperm Whale Bioacoustics
Bermant, P. C., Bronstein, M. M., Wood, R. J., Gero, S., & Gruber, D. F. (2019) · Scientific Reports · doi:10.1038/s41598-019-48909-4 · Cited by 155 · see citing works
scikit‐maad: An open‐source and modular toolbox for quantitative soundscape analysis in Python
Ulloa, J. S., Haupert, S., Latorre, J. F., Aubin, T., & Sueur, J. (2021) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13711 · Cited by 100 · see citing works
Automated identification of avian vocalizations with deep convolutional neural networks
Ruff, Z. J., Lesmeister, D. B., Duchac, L. S., Padmaraju, B. K., & Sullivan, C. M. (2019) · Remote Sensing in Ecology and Conservation · doi:10.1002/rse2.125 · Cited by 98 · see citing works
Tools for automated acoustic monitoring within the R package monitoR
Katz, J., Hafner, S. D., & Donovan, T. M. (2016) · Bioacoustics · doi:10.1080/09524622.2016.1138415 · Cited by 85 · see citing works
Cole, J. S., Michel, N. L., Emerson, S. A., & Siegel, R. B. (2022) · Ornithological Applications · doi:10.1093/ornithapp/duac003 · Cited by 71 · see citing works
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 70 · see citing works
Knight, E. C., & Bayne, E. M. (2018) · Bioacoustics · doi:10.1080/09524622.2018.1503971 · Cited by 58 · see citing works
Acoustic detection of regionally rare bird species through deep convolutional neural networks
Zhong, M., Taylor, R., Bates, N., Christey, D., Basnet, H., Flippin, J. L., Palkovitz, S., Dodhia, R. M., & Ferres, J. L. (2021) · Ecological Informatics · doi:10.1016/j.ecoinf.2021.101333 · Cited by 58 · see citing works
Improve automatic detection of animal call sequences with temporal context
Madhusudhana, S., Shiu, Y., Klinck, H., Fleishman, E., Liu, X., Nosal, E., Helble, T. A., Cholewiak, D., Gillespie, D. M., Širović, A., et al. (2021) · Journal of The Royal Society Interface · doi:10.1098/rsif.2021.0297 · Cited by 51 · see citing works
Advancing bird survey efforts through novel recorder technology and automated species identification
Toenies, M., & Rich, L. N. (2021) · California Fish and Wildlife Journal · doi:10.51492/cfwj.107.5 · Cited by 47 · see citing works
ANIMAL-SPOT enables animal-independent signal detection and classification using deep learning
Bergler, C., Smeele, S. Q., Tyndel, S. A., Barnhill, A., Ortiz, S. T., Kalan, A. K., Cheng, R. X., Brinkløv, S., Osiecka, A. N., Tougaard, J., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-26429-y · Cited by 45 · see citing works
Overview of BirdCLEF 2020: Bird Sound Recognition in Complex Acoustic Environments
Kahl, S., Clapp, M. K., Hopping, W. A., Goëau, H., Glotin, H., Planqué, R., Vellinga, W.-P., & Joly, A. (2020) · Agritrop (Cirad) · Cited by 43 · see citing works
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Lapp, S., Wu, T., Richards‐Zawacki, C. L., Voyles, J., Rodriguez, K. M., Shamon, H., & Kitzes, J. A. (2021) · Conservation Biology · doi:10.1111/cobi.13718 · Cited by 42 · see citing works
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Katsis, L. K. D., Hill, A. P., Piña‐Covarrubias, E., Prince, P., Rogers, A., Doncaster, C. P., & Snaddon, J. L. (2022) · Ecological Indicators · doi:10.1016/j.ecolind.2022.109128 · Cited by 39 · see citing works
Marsland, S., Priyadarshani, N., Juodakis, J., & Castro, I. (2019) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13213 · Cited by 33 · see citing works
Chronister, L. M., Rhinehart, T. A., Place, A., & Kitzes, J. A. (2021) · Ecology · doi:10.1002/ecy.3329 · Cited by 24 · see citing works
Self-citationNolan, V., Scott, C. D., Yeiser, J. M., Wilhite, N. G., Howell, P. E., Ingram, D. R., & Martin, J. A. (2022) · Remote Sensing in Ecology and Conservation · doi:10.1002/rse2.294 · Cited by 22 · see citing works
Silva, B., Mestre, F., Barreiro, S., Alves, P. J., & Herrera, J. M. (2022) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13964 · Cited by 14 · see citing works
Ketos—A deep learning package for creating acoustic detectors and classifiers
Kirsebom, O. S., Frazao, F. S., Padovese, B., Sakib, S., & Matwin, S. (2021) · The Journal of the Acoustical Society of America · doi:10.1121/10.0007998 · Cited by 11 · see citing works
Madhusudhana, S. (2021) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.5168314 · Cited by 11 · see citing works
Comparison of methods for automated identification of wild turkey gobbles
Wightman, P. H., Henrichs, D. W., Collier, B. A., & Chamberlain, M. J. (2022) · Wildlife Society Bulletin · doi:10.1002/wsb.1246 · Cited by 9 · see citing works
Nicholson, D. (2023) · The Journal of Open Source Software · doi:10.21105/joss.05338 · Cited by 8 · see citing works
Automated recognition of ruffed grouse drumming in field recordings
Lapp, S., Larkin, J. L., Parker, H. A., Larkin, J. T., Shaffer, D. R., Tett, C., McNeil, D. J., Fiss, C. J., & Kitzes, J. A. (2022) · Wildlife Society Bulletin · doi:10.1002/wsb.1395 · Cited by 7 · see citing works
Self-citationGIBBONFINDR: An R package for the detection and classification of acoustic signalsPreprint
Clink, D. J., & Klinck, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.02572 · Cited by 6 · see citing works
kitzeslab/opensoundscape: v0.10.0
Lapp, S., Rhinehart, T. A., Moore, B., Freeland‐Haynes, L., Guzman, S. R., Khilnani, J., Kitzes, J., Alexandra, & LeonardoViotti (2023) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.8170076 · Cited by 1 · see citing works
Self-citationkitzeslab/demos-for-opso: v1.0.0
Sammlapp, Khilnani, J., Freeland‐Haynes, L., A., & Rhinehart, T. A. (2023) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.8170061 · Cited by 1 · see citing works
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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 · see citing works
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Fiss, C. J., Lapp, S., Cohen, J. B., Parker, H. A., Larkin, J. T., Larkin, J. L., & Kitzes, J. A. (2024) · Ecosphere · doi:10.1002/ecs2.4954 · Cited by 16 · see citing works
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Larkin, J. T., McNeil, D. J., Chronister, L. M., Akresh, M. E., Cohen, E. B., D’Amato, A. W., Fiss, C. J., Kitzes, J. A., Larkin, J. L., Parker, H. A., et al. (2024) · Journal of Environmental Management · doi:10.1016/j.jenvman.2024.121786 · Cited by 14 · see citing works
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Self-citationKurtin, A., Gómez, E., Hussey, N., Noson, A. C., O'Reilly, M., Rhinehart, T. A., Skone, B., Wengappuly, B., & Boyce, A. J. (2025) · Conservation Science and Practice · doi:10.1111/csp2.70150 · Cited by 2 · see citing works
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Larkin, J. T., Itter, M. S., Fiss, C. J., Chronister, L. M., Kitzes, J. A., Larkin, J. L., Larkin, H. P., McNeil, D. J., D’Amato, A. W., Akresh, M. E., et al. (2025) · Forest Ecology and Management · doi:10.1016/j.foreco.2025.122988 · Cited by 2 · see citing works
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Gratwicke, B., Guerrel, J., Garcés, O., Illueca, E., Weisenbeck, N. J., Deichmann, J. L., & Roberto, I. (2026) · Frontiers in Amphibian and Reptile Science · doi:10.3389/famrs.2025.1736880 · Cited by 1 · see citing works
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Wagner, M., & Vashisth, D. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-93429-2_16
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Freeland‐Haynes, L., Weldy, M. J., Navine, A. K., Hart, P. J., Denton, T., & Kitzes, J. A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.08.747031
Self-citationFiss, C. J., Chronister, L. M., Hodges, T. J., Larkin, J. T., Logan, N., Lynch, C., McNeil, D. J., Rowse, L., Syunkova, A., Kitzes, J. A., et al. (2026) · Ecological Informatics · doi:10.1016/j.ecoinf.2026.104074
Self-citationA Preview of Computational Animal Linguistics
Dang, T. M., Desai, A., Lekhak, H., Panunto, T., Pike, L., Wang, S., & Zhu, K. Q. (2026) · ACM Computing Surveys · doi:10.1145/3850160
Stridulant: A Python toolkit for detecting hidden ecological processes through sound
van Steijn, T. L. H., Rodriguez-Martinez, S., & Klaminder, J. (2026) · Ecological Informatics · doi:10.1016/j.ecoinf.2026.104072
Holiman, H., Keever, A. C., Fournier, A. M. V., Blake‐Bradshaw, A. G., Hanni, D. J., & Cohen, B. S. (2026) · Journal of Wildlife Management · doi:10.1002/jwmg.70299
- Authors
- Lapp, S., Rhinehart, T., Freeland‐Haynes, L., Khilnani, J., Syunkova, A., & Kitzes, J.
- Affiliations
- University of Pittsburgh (US)
- Corresponding author
- Lapp, Sam
- Venue
- Methods in Ecology and Evolution
- Publisher
- Wiley
- Published
- 2023-08-09
- Volume
- 14
- Issue
- 9
- Pages
- 2321-2328
- ISSN
- 2041-210X (ISSN-L)
- OpenAlex
- W4385709007
- Cited by
- 57
- References
- 38
- Citations by year
- 2026: 18 · 2025: 20 · 2024: 18 · 2023: 1
- FWCI
- 6.88
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Methods in Ecology and Evolution (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Gordon and Betty Moore Foundation (72173)
- Mascaro Center for Sustainable Innovation, University of Pittsburgh
- National Fish and Wildlife Foundation (70153, 72173, 73615)
- National Geographic Society (NGS‐55651T‐18)
- National Science Foundation (1935507, 2120084, ACI‐1548562)
- Center for Research Computing, University of Pittsburgh
- Topics
- Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Developmental Biology › Animal Vocal Communication and Behavior
- Physical Sciences › Environmental Science › Ecology › Marine animal studies overview
- Keywords
- bioacoustics, passive acoustic monitoring, sound localization, species classification, biodiversity monitoring, Python, open source software, phenology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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