Have a personal or library account? Click to login
A Successful Crowdsourcing Approach for Bird Sound Classification Cover

A Successful Crowdsourcing Approach for Bird Sound Classification

Open Access
|Apr 2023

References

  1. 1BirdLife Finland. 2022. Liity jäseneksi. Available at: https://www.birdlife.fi/liitytaitue/liity/. [Last accessed 24 August 2022].
  2. 2Bonney, R, Shirk, JL, Phillips, TB, Wiggins, A, Ballard, HL, Miller-Rushing, AJ and Parrish, JK. 2014. Next Steps for Citizen Science. Science, 343(6178): 14361437. DOI: 10.1126/science.1251554
  3. 3Callaghan, CT, Poore, AGB, Mesaglio, T, Moles, AT, Nakagawa, S, Roberts, C, Cornwell, WK, et al. 2021. Three Frontiers for the Future of Biodiversity Research Using Citizen Science Data. BioScience, 71(1): 5563. DOI: 10.1093/biosci/biaa131
  4. 4Camargo, U, Roslin, T and Ovaskainen, O. 2019. Spatio-temporal scaling of biodiversity in acoustic tropical bird communities. Ecography, 42(11): 19361947. DOI: 10.1111/ecog.04544
  5. 5Constantine, M and The Sound Approach. 2006. The Sound Approach to birding. Dorset, UK: The Sound Approach.
  6. 6Cornell Lab of Ornithology. 2022. Macaulay Library. Available at https://www.macaulaylibrary.org/. [Last accessed 24 August 2022].
  7. 7Farina, A, Pieretti, N and Piccioli, L. 2011. The soundscape methodology for long-term bird monitoring: A Mediterranean Europe case-study. Ecological Informatics, 6(6): 354363. DOI: 10.1016/j.ecoinf.2011.07.004
  8. 8Franzoni, C, Poetz, M and Sauermann, H. 2021. Crowds, citizens, and science: a multi-dimensional framework and agenda for future research. Industry and Innovation, 134. DOI: 10.1080/13662716.2021.1976627
  9. 9Franzoni, C and Sauermann, H. 2014. Crowd science: The organization of scientific research in open collaborative projects. Research Policy, 43(1): 120. DOI: 10.1016/j.respol.2013.07.005
  10. 10Gibb, R, Browning, E, Glover-Kapfer, P & Jones, KE. 2019. Emerging opportunities and challenges for passive acoustics in ecological assessment and monitoring. Methods in Ecology and Evolution, 10(2): 169185. DOI: 10.1111/2041-210X.13101
  11. 11Hill, AP, Prince, P, Piña Covarrubias, E, Doncaster, CP, Snaddon, JL and Rogers, A. 2018. AudioMoth: Evaluation of a smart open acoustic device for monitoring biodiversity and the environment. Methods in Ecology and Evolution, 9(5): 11991211. DOI: 10.1111/2041-210X.12955
  12. 12Kahl, S, Wood, CM, Eibl, M and Klinck, H. 2021. BirdNET: A deep learning solution for avian diversity monitoring. Ecological Informatics, 61: 101236. DOI: 10.1016/j.ecoinf.2021.101236
  13. 13Krause, B and Farina, A. 2016. Using ecoacoustic methods to survey the impacts of climate change on biodiversity. Biological Conservation, 195: 245254. DOI: 10.1016/j.biocon.2016.01.013
  14. 14Land-Zandstra, A, Agnello, G and Gültekin, YS. 2021. Participants in Citizen Science. In K. Vohland, A. Land-Zandstra, L. Ceccaroni, R. Lemmens, J. Perelló, M. Ponti, R. Samson, & K. Wagenknecht (Eds.), The Science of Citizen Science (pp. 243259). Cham: Springer International Publishing. DOI: 10.1007/978-3-030-58278-4_13
  15. 15Lauha, P, Somervuo, P, Lehikoinen, P, Seibold, S, Geres, L, Richter, T and Ovaskainen, O. 2022. Domain-specific neural networks improve automated bird sound recognition already with small amount of local data. Methods in Ecology and Evolution, 13: 27992810. DOI: 10.1111/2041-210X.14003
  16. 16LeBien, J, Zhong, M, Campos-Cerqueira, M, Velev, JP, Dodhia, R, Ferres, JL and Aide, TM. 2020. A pipeline for identification of bird and frog species in tropical soundscape recordings using a convolutional neural network. Ecological Informatics, 59: 101113. DOI: 10.1016/j.ecoinf.2020.101113
  17. 17Lintott, C, Schawinski, K, Bamford, S, Slosar, A, Land, K, Thomas, D, Vandenberg, J, et al. 2011. Galaxy Zoo 1: data release of morphological classifications for nearly 900 000 galaxies*. Monthly Notices of the Royal Astronomical Society, 410(1): 166178. DOI: 10.1111/j.1365-2966.2010.17432.x
  18. 18Lintott, CJ, Schawinski, K, Slosar, A, Land, K, Bamford, S, Thomas, D, Vandenberg, J, et al. 2008. Galaxy Zoo: morphologies derived from visual inspection of galaxies from the Sloan Digital Sky Survey*. Monthly Notices of the Royal Astronomical Society, 389(3): 11791189. DOI: 10.1111/j.1365-2966.2008.13689.x
  19. 19Loiselle, BA, Howell, CA, Graham, CH, Goerck, JM, Brooks, T, Smith, KG and Williams, PH. 2003. Avoiding Pitfalls of Using Species Distribution Models in Conservation Planning. Conservation Biology, 17(6): 15911600. DOI: 10.1111/j.1523-1739.2003.00233.x
  20. 20Mengersen, K, Peterson, EE, Clifford, S, Ye, N, Kim, J, Bednarz, T, Hunter, V, et al. 2017. Modelling imperfect presence data obtained by citizen science. Environmetrics, 28(5): e2446. DOI: 10.1002/env.2446
  21. 21Ovaskainen, O, Moliterno de Camargo, U and Somervuo, P. 2018. Animal Sound Identifier (ASI): software for automated identification of vocal animals. Ecology Letters, 21(8): 12441254. DOI: 10.1111/ele.13092
  22. 22Papadopoulos, T, Roberts, S and Willis, K. 2015. Detecting bird sound in unknown acoustic background using crowdsourced training data. arXiv preprint arXiv:1505.06443.
  23. 23Ruff, ZJ, Lesmeister, DB, Duchac, LS, Padmaraju, BK and Sullivan, CM. 2020. Automated identification of avian vocalizations with deep convolutional neural networks. Remote Sensing in Ecology and Conservation, 6(1): 7992. DOI: 10.1002/rse2.125
  24. 24Salamon, J and Bello, JP. 2017. Deep convolutional neural networks and data augmentation for environmental sound classification. IEEE Signal processing letters, 24(3): 279283. DOI: 10.1109/LSP.2017.2657381
  25. 25Snyder, R, Clark, M, Salas, L, Schackwitz, W, Leland, D, Stephens, T, Clas, K, et al. 2022. The Soundscapes to Landscapes Project: Development of a Bioacoustics-Based Monitoring Workflow with Multiple Citizen Scientist Contributions. Citizen Science: Theory and Practice, 7(1): 24. DOI: 10.5334/cstp.391
  26. 26Stevenson, RD, Suomela, T, Kim, H and He, Y. 2021. Seven Primary Data Types in Citizen Science Determine Data Quality Requirements and Methods. Frontiers in Climate, 3. DOI: 10.3389/fclim.2021.645120
  27. 27Stowell, D, Wood, MD, Pamuła, H, Stylianou, Y and Glotin, H. 2019. Automatic acoustic detection of birds through deep learning: The first Bird Audio Detection challenge. Methods in Ecology and Evolution, 10(3): 368380. DOI: 10.1111/2041-210X.13103
  28. 28Van Brussel, S and Huyse, H. 2019. Citizen science on speed? Realising the triple objective of scientific rigour, policy influence and deep citizen engagement in a large-scale citizen science project on ambient air quality in Antwerp. Journal of Environmental Planning and Management, 62(3): 534551. DOI: 10.1080/09640568.2018.1428183
  29. 29Walmsley, M, Smith, L, Lintott, C, Gal, Y, Bamford, S, Dickinson, H, Wright, D, et al. 2020. Galaxy Zoo: probabilistic morphology through Bayesian CNNs and active learning. Monthly Notices of the Royal Astronomical Society, 491(2): 15541574. DOI: 10.1093/mnras/stz2816
  30. 30Warblr Ltd. 2022. Warblr: Identify UK bird songs. 25.1.2022. Available at https://www.warblr.co.uk/. [Last accessed 24 August 2022].
  31. 31Xeno-canto Foundation. 2022. Xeno-Canto – sharing bird sounds from around the world. 25.1.2022. Available at https://xeno-canto.org/. [Last accessed 24 August 2022].
DOI: https://doi.org/10.5334/cstp.556 | Journal eISSN: 2057-4991
Language: English
Submitted on: Aug 25, 2022
Accepted on: Mar 7, 2023
Published on: Apr 11, 2023
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2023 Petteri Lehikoinen, Meeri Rannisto, Ulisses Camargo, Aki Aintila, Patrik Lauha, Esko Piirainen, Panu Somervuo, Otso Ovaskainen, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.