Bird species recognition using support vector machines
Open accessAbstract
Automatic identification of bird species by their vocalization is studied in this paper. Bird sounds are represented with two different parametric representations: (i) the mel-cepstrum parameters and (ii) a set of low-level signal parameters, both of which have been found useful for bird species recognition. Recognition is performed in a decision tree with support vector machine (SVM) classifiers at each node that perform classification between two species. Recognition is tested with two sets of bird species whose recognition has been previously tested with alternative methods. Recognition results with the proposed method suggest better or equal performance when compared to existing reference methods.
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Classification of calls from different species and subspecies is robust.
“Classification of calls from different species and subspecies is robust (Fagerlund, 2007;Kershenbaum et al., 2016)”
From Applications of machine learning in animal behaviour studies (2017) · cites this paper as Fagerlund, 2007
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Research works printed but not linked: 2
- S. Fagerlund, "Automatic recognition of bird species by their sounds," M.S. thesis, Helsinki University of Technology, Es- poo, Finland, 2004.
- J. C. Platt, N. Cristianini, and J. Shawe-Taylor, "Large margin dags for multiclass classification," in Advances in Neural Infor- mation Processing Systems 12, pp. 547-553, MIT Press, Cam- bridge, Mass, USA, 2000.
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“Classification of calls from different species and subspecies is robust (Fagerlund, 2007; Kershenbaum et al., 2016) and permits assessment of community structure (e.”
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- Authors
- Fagerlund, S.
- Corresponding author
- Fagerlund, Seppo
- Venue
- EURASIP Journal on Advances in Signal Processing
- Published
- 2007-05-22
- Volume
- 2007
- Issue
- 1
- ISSN
- 1110-8657 (ISSN-L)
- 1687-6180
- OpenAlex
- W2019432531
- Cited by
- 276
- References
- 34
- Citations by year
- 2026: 12 · 2025: 13 · 2024: 11 · 2023: 15 · 2022: 12 · 2021: 12 · 2020: 18 · 2019: 10 · 2018: 21 · 2017: 26 · 2016: 21 · 2015: 16 · 2014: 12 · 2013: 24 · 2012: 17
- FWCI
- 11.12
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- EURASIP Journal on Advances in Signal Processing (publishedVersion, cc-by)
- Aaltodoc (Aalto University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Academy of Finland
- 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 › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Keywords
- bird identification, bird vocalizations, automatic species identification, mel-cepstrum, support vector machine, decision tree classification
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