Automated classification of bird and amphibian calls using machine learning: A comparison of methods
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Machine learning has been applied to classify and count particular elements or syllables in vocalization recordings.
“Instead, ML has been applied to classify and count particular elements or syllables (Acevedo, Corrada-Bravo, Corrada-Bravo, Villanueva-Rivera, & Aide, 2009).”
From Applications of machine learning in animal behaviour studies (2017) · cites this paper as Acevedo et al., 2009
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- Authors
- Acevedo, M. A., Corrada-Bravo, C. J., Corrada-Bravo, H., Villanueva-Rivera, L. J., & Aide, T. M.
- Affiliations
- University of Puerto Rico System (PR)
- Purdue University West Lafayette (US)
- Corresponding author
- Acevedo, Miguel A.
- Venue
- Ecological Informatics
- Publisher
- Elsevier BV
- Published
- 2009-09
- Volume
- 4
- Issue
- 4
- Pages
- 206-214
- ISSN
- 1574-9541 (ISSN-L)
- OpenAlex
- W2039261343
- Cited by
- 317
- References
- 38
- Citations by year
- 2026: 10 · 2025: 10 · 2024: 13 · 2023: 21 · 2022: 14 · 2021: 22 · 2020: 21 · 2019: 22 · 2018: 31 · 2017: 38 · 2016: 30 · 2015: 21 · 2014: 21 · 2013: 23 · 2012: 11
- FWCI
- 4.39
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Ecological Informatics (publishedVersion)
- WBI Studies Repository (submittedVersion)
- Funders
- National Science Foundation
- U.S. Department of Defense
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Developmental Biology › Animal Vocal Communication and Behavior
- Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
- Physical Sciences › Environmental Science › Ecology › Marine animal studies overview
- Keywords
- automated species identification, frog vocalization, bird calls, amphibian vocalization, call classification, support vector machine, decision tree, linear discriminant analysis, call structure, acoustic biodiversity monitoring
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