Holger Klinck, Ph.D.

Dr. Holger Klinck
Holger Klinck

I am the John W. Fitzpatrick Director of the K. Lisa Yang Center for Conservation Bioacoustics at the Cornell Lab of Ornithology. I joined the Lab in December 2015 and took over the directorship of the Yang Center in August 2016. I am also a Faculty Fellow with the Atkinson Center for a Sustainable Future at Cornell University. In addition, I hold a Courtesy Professor position at Oregon State University (OSU). Before moving to the U.S. in early 2008 for a postdoctoral position at OSU, I was a Ph.D. student at the Alfred Wegener Institute for Polar and Marine Research in Germany. My graduate work focused on the development of the Perennial Acoustic Observatory in the Antarctic Ocean and the study of the leopard seal (coolest animal ever!) vocal behavior. My current research focuses on the development and application of hard- and software tools for passive-acoustic monitoring of terrestrial and marine ecosystems and biodiversity. One of my goals is to enable researchers around the globe to acoustically monitor habitats and wildlife at large spatial scales. I am also studying the impacts of anthropogenic noise on the vocal and locomotive behavior of animals.

I am a full member of the Acoustical Society of America (ASA) and the moderator of the popular Bioacoustics-L mailing list, which is hosted by the Yang Center. I am a manuscript referee for the Journal of the Acoustical Society of America, Marine Ecology Progress Series, Aquatic Mammals, Canadian Journal of Zoology, Ecological Informatics, Proceedings of the Royal Society B, Animal Behaviour, Animal Biotelemetry, Deep-Sea Research Part I, Polar Biology, Biology Letters, Plos ONE, Acoustics Australia, New Zealand Journal of Ecology, Nature Communications, PeerJ, IEEE Ocean Engineering, Sensors, Mammal Research, Scientific Reports, Methods in Ecology and Evolution, and Landscape Ecology. I am also refereeing proposals for NSF, National Geographic, NOAA, Seagrant New Hampshire, and the US Navy’s Living Marine Resources Program (LMR).

I advise several undergraduates, graduate students, and postdocs at Cornell and OSU and I am regularly teaching national and international bioacoustics classes.

I am an avid college and professional sports fan. My hobbies include running, sailing, and tinkering with gadgets. My wife Karolin and I live in Lansing, New York. I enjoy hiking with our two Australian shepherd dogs, Lilly and Sammy, and our miniature dachshund Marvin.

Year Hired: 2015

Contact Information
K. Lisa Yang Center for Conservation Bioacoustics
Cornell Lab of Ornithology, Room #142
159 Sapsucker Woods Road, Ithaca, NY 14850, USA
Phone: +1-607.254.6250
Email: Holger.Klinck@cornell.edu

Social Media: LinkedIn, Google Scholar.

Organization/Membership: Full member of the Acoustical Society of America

Recent Publications

Wood, C.M. et al. (2024) ‘A scalable and transferable approach to combining emerging conservation technologies to identify biodiversity change after large disturbances’, Journal of Applied Ecology, 61(4), pp. 797–808. Available at: https://doi.org/10.1111/1365-2664.14579.
Ghani, B. et al. (2023) ‘Global birdsong embeddings enable superior transfer learning for bioacoustic classification’, Scientific Reports, 13(1), p. 22876. Available at: https://doi.org/10.1038/s41598-023-49989-z.
Hopping, W.A. et al. (2023) ‘Simultaneous passive acoustic monitoring uncovers evidence of potentially overlooked temporal variation in an Amazonian bird community’, Ibis, n/a(n/a). Available at: https://doi.org/10.1111/ibi.13293.
White, E.L. et al. (2023) ‘One size fits all? Adaptation of trained CNNs to new marine acoustic environments’, Ecological Informatics, 78, p. 102363. Available at: https://doi.org/10.1016/j.ecoinf.2023.102363.
Kelly, K.G. et al. (2023) ‘Estimating population size for California spotted owls and barred owls across the Sierra Nevada ecosystem with bioacoustics’, Ecological Indicators, 154, p. 110851. Available at: https://doi.org/10.1016/j.ecolind.2023.110851.
Baker, C.S. et al. (2023) ‘Quantification by droplet digital PCR and species identification by metabarcoding of environmental (e)DNA from Blainville’s beaked whales, with assisted localization from an acoustic array’, PLOS ONE, 18(9), p. e0291187. Available at: https://doi.org/10.1371/journal.pone.0291187.
Kahl, S. et al. (2023) ‘Overview of BirdCLEF 2023: Automated Bird Species Identification in Eastern Africa 4.0’, in Working Notes of CLEF.
Soanes, L.M. et al. (2023) ‘Passive acoustic monitoring of birds in the Lesser Antilles—a useful tool for monitoring remote sites?’, Journal of Caribbean Ornithology, 36, pp. 62–74. Available at: https://doi.org/10.55431/jco.2023.36.62-74.
McCullough, J.L.K. et al. (2023) ‘Geographic distribution of the Cross Seamount beaked whale based on acoustic detections’, Marine Mammal Science [Preprint]. Available at: https://doi.org/10.1111/mms.13061.
Sethi, S.S. et al. (2023) ‘Limits to the accurate and generalizable use of soundscapes to monitor biodiversity’, Nature Ecology & Evolution, pp. 1–6. Available at: https://doi.org/10.1038/s41559-023-02148-z.
Li, P. et al. (2023) ‘Using deep learning to track time × frequency whistle contours of toothed whales without human-annotated training data’, The Journal of the Acoustical Society of America, 154(1), pp. 502–517. Available at: https://doi.org/10.1121/10.0020274.
Madhusudhana, S. et al. (2023) ‘A passive acoustic approach to impact assessment of large underwater explosions on marine fauna — planning, analyses, and lessons learned’, in OCEANS 2023 - Limerick. OCEANS 2023 - Limerick, pp. 1–7. Available at: https://doi.org/10.1109/OCEANSLimerick52467.2023.10244589.
D’Souza, M.L. et al. (2023) ‘Arabian Sea Humpback Whale (Megaptera novaeangliae) Singing Activity off Netrani Island, India’, Aquatic Mammals, 49(3), pp. 223–235. Available at: https://doi.org/10.1578/AM.49.3.2023.223.
Fleishman, E. et al. (2023) ‘Ecological inferences about marine mammals from passive acoustic data’, Biological Reviews, 98(5). Available at: https://onlinelibrary.wiley.com/doi/full/10.1111/brv.12969 (Accessed: 7 May 2024).
McGinn, K. et al. (2023) ‘Feature embeddings from the BirdNET algorithm provide insights into avian ecology’, Ecological Informatics, 74, p. 101995. Available at: https://doi.org/10.1016/j.ecoinf.2023.101995.
Kennedy, A.G. et al. (2023) ‘Evidence for acoustic niche partitioning depends on the temporal scale in two sympatric Bornean hornbill species’, Biotropica, n/a(n/a). Available at: https://doi.org/10.1111/btp.13205.
Li, P. et al. (2023) ‘Learning Stage-wise GANs for Whistle Extraction in Time-Frequency Spectrograms’, IEEE Transactions on Multimedia, pp. 1–13. Available at: https://doi.org/10.1109/TMM.2023.3251109.
Barlow, D.R. et al. (2023) ‘Environmental conditions and marine heatwaves influence blue whale foraging and reproductive effort’, Ecology and Evolution, 13(2), p. e9770. Available at: https://doi.org/10.1002/ece3.9770.
Clink, D.J. et al. (2023) ‘A workflow for the automated detection and classification of female gibbon calls from long-term acoustic recordings’, Frontiers in Ecology and Evolution, 11. Available at: https://www.frontiersin.org/articles/10.3389/fevo.2023.1071640 (Accessed: 9 February 2023).
Rameau, A. et al. (2023) ‘Acoustic Screening of the “Wet voice”: Proof of Concept in an ex vivo Canine Laryngeal Model’, The Laryngoscope, 133(10), pp. 2517–2524. Available at: https://doi.org/10.1002/lary.30525.
Rameau, A. et al. (2023) ‘Changes in Cough Airflow and Acoustics After Injection Laryngoplasty’, The Laryngoscope, 133(S3), pp. S1–S14. Available at: https://doi.org/10.1002/lary.30255.
Joly, A. et al. (2023) ‘Overview of LifeCLEF 2023: Evaluation of AI Models for the Identification and Prediction of Birds, Plants, Snakes and Fungi’, in A. Arampatzis et al. (eds) Experimental IR Meets Multilinguality, Multimodality, and Interaction. Cham: Springer Nature Switzerland (Lecture Notes in Computer Science), pp. 416–439. Available at: https://doi.org/10.1007/978-3-031-42448-9_27.
Ghani, B., Klinck, H. and Hallerberg, S. (2022) ‘Classification of group-specific variations in songs within House Wren species using machine learning models’, Ecological Informatics, p. 101946. Available at: https://doi.org/10.1016/j.ecoinf.2022.101946.
Conant, P.C. et al. (2022) ‘Silbido profundo: An open source package for the use of deep learning to detect odontocete whistles’, The Journal of the Acoustical Society of America, 152(6), pp. 3800–3808. Available at: https://doi.org/10.1121/10.0016631.
Fregosi, S. et al. (2022) ‘Detection probability and density estimation of fin whales by a Seaglider’, The Journal of the Acoustical Society of America, 152(4), pp. 2277–2291. Available at: https://doi.org/10.1121/10.0014793.
Kahl, S. et al. (2022) ‘Overview of BirdCLEF 2022: Endangered bird species recognition in soundscape recordings’, in. CLEF 2022 - Conference and Labs of the Evaluation Forum, p. 1929. Available at: https://hal.inrae.fr/hal-03791428 (Accessed: 16 December 2022).
Joly, A. et al. (2022) ‘Overview of LifeCLEF 2022: an evaluation of Machine-Learning based Species Identification and Species Distribution Prediction’, CLEF 2022 - Conference and Labs of the Evaluation Forum [Preprint].
Barlow, D.R. et al. (2022) ‘Shaken, not stirred: blue whales show no acoustic response to earthquake events’, Royal Society Open Science, 9(7), p. 220242. Available at: https://doi.org/10.1098/rsos.220242.
Clink, D.J. et al. (2022) ‘Tarsier islands: Exploring patterns of variation in tarsier duets from offshore islands of North Sulawesi’, American Journal of Primatology, n/a(n/a), p. e23410. Available at: https://doi.org/10.1002/ajp.23410.
Reid, D.S. et al. (2022) ‘Breeding status shapes territoriality and vocalization patterns in spotted owls’, Journal of Avian Biology, 8(e02952). Available at: https://doi.org/10.1111/jav.02952.
Symes, L.B. et al. (2022) ‘Estimation of katydid calling activity from soundscape recordings’, Journal of Orthoptera Research, 31(2), pp. 173–180. Available at: https://doi.org/10.3897/jor.31.73373.
Palmer, K.J. et al. (2022) ‘Accounting for the Lombard effect in estimating the probability of detection in passive acoustic surveys: Applications for single sensor mitigation and monitoring’, The Journal of the Acoustical Society of America, 151(1), pp. 67–79. Available at: https://doi.org/10.1121/10.0009168.
Tolkova, I. and Klinck, H. (2022) ‘Source separation with an acoustic vector sensor for terrestrial bioacoustics’, The Journal of the Acoustical Society of America, 152(2), pp. 1123–1134. Available at: https://doi.org/10.1121/10.0013505.
Wood, C.M. et al. (2022) ‘The machine learning–powered BirdNET App reduces barriers to global bird research by enabling citizen science participation’, PLOS Biology, 20(6), p. e3001670. Available at: https://doi.org/10.1371/journal.pbio.3001670.
Tuia, D. et al. (2022) ‘Perspectives in machine learning for wildlife conservation’, Nature Communications, 13(1), p. 792. Available at: https://doi.org/10.1038/s41467-022-27980-y.
Mirin, B.H. and Klinck, H. (2021) ‘Bird singing contests: Looking back on thirty years of research on a global conservation concern’, Global Ecology and Conservation, 30, p. e01812. Available at: https://doi.org/10.1016/j.gecco.2021.e01812.
Kendall-Bar, J. et al. (2021) ‘Visualizing Life in the Deep: A Creative Pipeline for Data-Driven Animations to Facilitate Marine Mammal Research, Outreach, and Conservation’, IEEE, VISAP.
Kahl, S. et al. (2021) ‘Overview of BirdCLEF 2021: Bird call identification in soundscape recordings’, CLEF 2021 [Preprint].
Miksis-Olds, J.L. et al. (2021) ‘Ocean Sound Analysis Software for Making Ambient Noise Trends Accessible (MANTA)’, Frontiers in Marine Science, 8, p. 1144. Available at: https://doi.org/10.3389/fmars.2021.703650.
Reid, D.S. et al. (2021) ‘Noisy neighbors and reticent residents: Distinguishing resident from non-resident individuals to improve passive acoustic monitoring’, Global Ecology and Conservation, 28, p. e01710. Available at: https://doi.org/10.1016/j.gecco.2021.e01710.
Duporge, I. et al. (2021) ‘Determination of optimal flight altitude to minimise acoustic drone disturbance to wildlife using species audiograms’, Methods in Ecology and Evolution, n/a(n/a). Available at: https://doi.org/10.1111/2041-210X.13691.
Madhusudhana, S. et al. (2021) ‘Improve automatic detection of animal call sequences with temporal context’, Journal of The Royal Society Interface, 18(180), p. 20210297. Available at: https://doi.org/10.1098/rsif.2021.0297.
Gabriele, C.M., Ponirakis, D.W. and Klinck, H. (2021) ‘Underwater Sound Levels in Glacier Bay During Reduced Vessel Traffic Due to the COVID-19 Pandemic’, Frontiers in Marine Science, 8. Available at: https://doi.org/10.3389/fmars.2021.674787.
Barlow, D.R. et al. (2021) ‘Temporal and spatial lags between wind, coastal upwelling, and blue whale occurrence’, Scientific Reports, 11(6915). Available at: https://doi.org/doi.org/10.1038/s41598-021-86403-y.
Fournet, M.E.H. et al. (2021) ‘Limited vocal compensation for elevated ambient noise in bearded seals: implications for an industrializing Arctic Ocean’, Proceedings of the Royal Society B, 288(1945). Available at: https://doi.org/10.1098/rspb.2020.2712.
Clink, D. et al. (2021) ‘Not by the light of the moon: Investigating circadian rhythms and environmental predictors of calling in Bornean great argus’, PLoS ONE, 16(2), p. e0246564. Available at: https://doi.org/https://doi.org/10.1371/journal.pone.0246564.
Wood, C.M. et al. (2021) ‘Survey coverage, recording duration and community composition affect observed species richness in passive acoustic surveys’, Methods in Ecology and Evolution [Preprint]. Available at: https://doi.org/10.1111/2041-210X.13571.
Wood, C.M. et al. (2021) ‘Illuminating the Nocturnal Habits of Owls with Emerging Tagging Technologies’, Wildlife Society Bulletin [Preprint]. Available at: https://doi.org/10.1002/wsb.1156.
Kahl, S. et al. (2021) ‘BirdNET: A deep learning solution for avian diversity monitoring’, Ecological Informatics [Preprint]. Available at: https://doi.org/10.1016/j.ecoinf.2021.101236.
Martin, S.B. et al. (2021) ‘Hybrid millidecade spectra: A practical format for exchange of long-term ambient sound data’, JASA Express Letters, 1(1).
Injaian, A.S., Lane, E.D. and Klinck, H. (2021) ‘Aircraft events correspond with vocal behavior in a passerine’, Scientific Reports [Preprint]. Available at: https://doi.org/10.1038/s41598-020-80380-4.
Clink, D.J. and Klinck, H. (2020) ‘Unsupervised acoustic classification of individual gibbon females and the implications for passive acoustic monitoring’, Methods in Ecology and Evolution [Preprint]. Available at: https://doi.org/10.1111/2041-210X.13520.
Fregosi, S. et al. (2020) ‘Detections of Whale Vocalizations by Simultaneously Deployed Bottom-Moored and Deep-Water Mobile Autonomous Hydrophones’, Frontiers in Marine Science, 7(721). Available at: https://doi.org/10.3389/fmars.2020.00721.
Dziak, R. et al. (2020) ‘Deep Ocean Passive Acoustic Technologies for Exploration of Ocean and Surface Sea Worlds in the Outer Solar System’, Oceanography [Preprint]. Available at: https://doi.org/10.5670/oceanog.2020.221.
Clink, D.J., Tasirin, J.S. and Klinck, H. (2020) ‘Vocal individuality and rhythm in male and female duet contributions of a nonhuman primate’, Current Zoology, 66(2), pp. 173–186. Available at: https://doi.org/10.1093/cz/zoz035.
Shiu, Y. et al. (2020) ‘Deep neural networks for automated detection of marine mammal species’, Scientific Report, 10(607). Available at: https://doi.org/10.1038/s41598-020-57549-y.
Nieukirk, S. et al. (2020) ‘Multi-year occurrence of sei whale calls in North Atlantic polar waters’, The Journal of the Acoustical Society of America, 147(1842). Available at: https://doi.org/doi.org/10.1121/10.0000931.
Matthews, L.P. et al. (2020) ‘Acoustically advertising male harbour seals in southeast Alaska do not make biologically relevant acoustic adjustments in the presence of vessel noise’, Biology Letters, 16(4). Available at: https://doi.org/10.1098/rsbl.2019.0795.
Klinck, H. et al. (2020) ‘The Rockhopper: a compact and extensible marine autonomous passive acoustic recording system’, Global Oceans 2020: Singapore – U.S. Gulf Coast, pp. 1–7. Available at: https://doi.org/10.1109/IEEECONF38699.2020.9388970.
Fregosi, S. et al. (2020) ‘Comparison of fin whale 20 Hz call detections by deep-water mobile autonomous and stationary recorders’, The Journal of the Acoustical Society of America, 147(2), pp. 961–977. Available at: https://doi.org/10.1121/10.0000617.
Davis, G.E. et al. (2020) ‘Exploring movement patterns and changing distributions of baleen whales in the western North Atlantic using a decade of passive acoustic data’, Global Change Biology, n/a(n/a). Available at: https://doi.org/10.1111/gcb.15191.
Bouffaut, L. et al. (2020) ‘A performance comparison of tonal detectors for low-frequency vocalizations of Antarctic blue whales’, The Journal of the Acoustical Society of America, 147(260). Available at: https://doi.org/10.1121/10.0000609.
Sethi, S.S. et al. (2020) ‘Characterizing soundscapes across diverse ecosystems using a universal acoustic feature set’, PNAS [Preprint]. Available at: https://doi.org/10.1073/pnas.2004702117.
Wood, C.M. et al. (2020) ‘Using the ecological significance of animal vocalizations to improve inference in acoustic monitoring programs’, Conservation Biology [Preprint]. Available at: https://doi.org/10.1111/cobi.13516.
Clink, D.J., Hamid Ahmad, A. and Klinck, H. (2020) ‘Gibbons aren’t singing in the rain: presence and amount of rainfall influences ape calling behavior in Sabah, Malaysia’, Scientific Reports, 10(1282). Available at: https://doi.org/10.1038/s41598-020-57976-x.
Clink, D.J., Hamid Ahmad, A. and Klinck, H. (2020) ‘Brevity is not a universal in animal communication: evidence for compression depends on the unit of analysis in small ape vocalizations’, Royal Society Open Science, 7(4). Available at: https://doi.org/10.1098/rsos.200151.
Matsumoto, H. et al. (2019) ‘Field testing and performance evaluation of the Long-term Acoustic Real-Time Sensor for Polar Areas (LARA)’, IEEE, Oceans 2019.
Diogou, N. et al. (2019) ‘Sperm whale (Physeter macrocephalus) acoustic ecology at Ocean Station PAPA (Gulf of Alaska) – Part 1: Detectability and seasonality’, Deep-Sea Research, Part I.
Diogou, N. et al. (2019) ‘Year-round acoustic presence of sperm whales (Physeter macrocephalus) and baseline ambient ocean sound levels at the Hellenic Trench and the North Aegean Trough, Greece’, Mediterranean Marine Science [Preprint]. Available at: https://doi.org/http://dx.doi.org/10.12681/mms.18769.
Bouffaut, L. et al. (2019) ‘Automated blue whale song transcription across variable acoustic contexts’, Oceans 2019 [Preprint].
Clink, D.J., Tasirin, J.S. and Klinck, H. (2019) ‘Vocal individuality and rhythm in male and female duet contributions of a nonhuman primate’, Current Zoology [Preprint]. Available at: https://doi.org/10.1093/cz/zoz035.
Wood, C.M. et al. (2019) ‘Detecting small changes in populations at landscape scales: a bioacoustic site-occupancy framework’, Ecological Indicators, 98, pp. 492–507. Available at: https://doi.org/https://doi.org/10.1016/j.ecolind.2018.11.018.
Holdman, A.K. et al. (2018) ‘Acoustic monitoring reveals the times and tides of harbor porpoise (Phocoena phocoena) distribution off central Oregon, U.S.A.’, Marine Mammal Science [Preprint]. Available at: https://doi.org/10.1111/mms.12537.
Haver, S.M. et al. (2018) ‘Monitoring long-term soundscape trends in U.S. Waters: The NOAA/NPS Ocean Noise Reference Station Network’, Marine Policy, 90, pp. 6–13.
Fournet, M.E.H. et al. (2018) ‘Humpback whales (Megaptera novaeangliae) alter calling behavior in response to natural sounds and vessel noise’, Marine Ecology Progress Series, 607, pp. 251–268. Available at: https://doi.org/10.3354/meps12784.
Fournet, M.E.H. et al. (2018) ‘More of the same: allopatric humpback whale populations share acoustic repertoire’, PeerJ, 6:e5365. Available at: https://doi.org/10.7717/peerj.5365.
Fournet, M.E.H. et al. (2018) ‘Source levels of foraging humpback whale calls’, Journal of the Acoustical Society of America, 143(2), p. EL105.
Fournet, M.E.H. et al. (2018) ‘Some things never change: multi-decadal stability in humpback whale calling repertoire on Southeast Alaskan foraging grounds’, Scientific Reports, 8(1). Available at: https://doi.org/10.1038/s41598-018-31527-x.
Barlow, J. et al. (2018) ‘Diving behavior of Cuvier’s beaked whales inferred from three-dimensional acoustic localization and tracking using a nested array of drifting hydrophone recorders’, The Journal of the Acoustical Society of America, 144(4), pp. 2030–2041. Available at: https://doi.org/10.1121/1.5055216.
Barlow, D.R. et al. (2018) ‘Documentation of a New Zealand blue whale population based on multiple lines of evidence’, Endangered Species Research, 36, pp. 27–40. Available at: https://doi.org/https://doi.org/10.3354/esr00891.
Baker, C.S. et al. (2018) ‘Environmental DNA (eDNA) From the Wake of the Whales: Droplet Digital PCR for Detection and Species Identification’, Frontiers in Marine Science, 5:133. Available at: https://doi.org/10.3389/fmars.2018.00133.
Kahl, S. et al. (2018) ‘Recognizing Birds from Sound - The 2018 BirdCLEF Baseline System’, Computer Vision and Pattern Recognition [Preprint]. Available at: https://doi.org/arXiv:1804.07177v1.
Bopardikar, I. et al. (2018) ‘Description and classification of Indian Ocean humpback dolphin (Sousa plumbea) whistles recorded off the Sindhudurg coast of Maharashtra, India’, Marine Mammal Science [Preprint]. Available at: https://doi.org/10.1111/mms.12479.