A comparative study in birds: Call-type-independent species and individual recognition using four machine-learning methods and two acoustic features
Abstract
Species- and individual-specific animal calls can be used in identification as verified in playback experiments and analyses of features extracted from these signals. The use of machine-learning methods and acoustic features borrowed from human speech recognition to identify animals at the species and individual level has increased recently. To date there have been few studies comparing the performances of these methods and features used for call-type-independent species and individual identification. We compared the performance of four machine-learning classifiers in the identification of ten passerine species, and individual identification for three passerines using two acoustic features. The methods did not require us to pre-categorize the component syllables in call-type-independent species and individual identification systems. The results of our experiment indicated that support vector machines (SVM) performed best generally, regardless of which acoustic feature was used, linear predictive coefficients (LPCs) increased the recognition accuracies of hidden Markov models (HMM) greatly, and the most appropriate classifiers for LPCs and Mel-frequency cepstral coefficients (MFCCs) were HMM and SVM respectively. This study will assist researchers in selecting classifiers and features to use in future species and individual recognition studies.
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Finer scale discriminations are possible at both the individual level and the bird song elements level.
“Finer scale discriminations are possible at both the individual level (Cheng, Xie, Lin, & Ji, 2012) and the bird song elements level (Ranjard & Ross, 2008).”
From Applications of machine learning in animal behaviour studies (2017) · cites this paper as Cheng et al., 2012
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- Authors
- Cheng, J., Xie, B., Lin, C., & Ji, L.
- Affiliations
- Chinese Academy of Sciences (CN)
- Corresponding author
- Ji, Liqiang
- Venue
- Bioacoustics
- Publisher
- Informa UK Limited
- Published
- 2012-06
- Volume
- 21
- Issue
- 2
- Pages
- 157-171
- ISSN
- 0952-4622 (ISSN-L)
- 2165-0586
- OpenAlex
- W2065265481
- Cited by
- 42
- References
- 59
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 5 · 2023: 2 · 2022: 5 · 2021: 2 · 2020: 2 · 2019: 3 · 2018: 3 · 2017: 1 · 2015: 1 · 2014: 3 · 2013: 4
- FWCI
- 1.98
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Bioacoustics (publishedVersion)
- 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 › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Keywords
- bird vocalizations, species identification, individual recognition, Passeriformes, machine learning classifiers, support vector machine, hidden Markov models, linear prediction coefficients, Mel-frequency cepstral coefficients, acoustic features, classifier comparison
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