On-Bird Sound Recordings: Automatic Acoustic Recognition of Activities and Contexts
Open accessAbstract
We introduce a novel approach to studying animal behavior and the context in which it occurs, through the use of microphone backpacks carried on the backs of individual free-flying birds. These sensors are increasingly used by animal behavior researchers to study individual vocalizations of freely behaving animals, even in the field. However, such devices may record more than an animal's vocal behavior, and have the potential to be used for investigating specific activities (movement) and context (background) within which vocalizations occur. To facilitate this approach, we investigate the automatic annotation of such recordings through two different sound scene analysis paradigms: A scene-classification method using feature learning, and an event-detection method using probabilistic latent component analysis. We analyze recordings made with Eurasian jackdaws (Corvus monedula) in both captive and field settings. Results are comparable with the state of the art in sound scene analysis; we find that the current recognition quality level enables scalable automatic annotation of audio logger data, given partial annotation, but also find that individual differences between animals and/or their backpacks limit the generalization from one individual to another. we consider the interrelation of “scenes” and “events” in this particular task, and issues of temporal resolution.
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Studies in other taxa such as birds, mammals, amphibians and arthropods suggest the context of acoustic signal production is an important factor affecting the parameters of acoustic signals measured.
“Studies in other taxa such as birds (Stowell et al. 2017), mammals (Feighny et al. 2006; Caselli et al. 2014), amphibians (Toledo et al. 2015) and arthropods (Fukutomi and Ogawa 2017; Dobai et al. 2018; Kerchev 2019) suggest the context of acoustic signal production is an important factor affecting the parameters of acoustic signals measured.”
From Roars, Rumbles, and Resonance: A Systematic Review and Meta‐Analysis of Crocodylian Acoustic Signals (2026) · cites this paper as Stowell et al. 2017
Wearable acoustic tags can capture vocalizations, movement-generated sounds, and environmental noise, enabling automated inference of behavioural states such as flying, diving, or resting from acoustic patterns alone.
“Wearable acoustic tags can capture not only vocalizations but also movement-generated sounds and environmental noise, enabling automated inference of behavioural states such as flying, diving, or resting from acoustic patterns alone”
From Listening forward: emerging roles of bioacoustics in ecology, evolution, and conservation (2026) · cites this paper as Stowell et al. 2017
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“…acoustic tags can capture not only vocalizations but also movement-generated sounds and environmental noise, enabling automated inference of behavioural states such as flying, diving, or resting from acoustic patterns alone (Stowell et al. 2017 ; Greif and Yovel 2019; Thiebault et al. 2021).”
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- Authors
- Stowell, D., Benetos, E., & Gill, L. F.
- Affiliations
- Queen Mary University of London (GB)
- Max Planck Society (DE)
- Venue
- IEEE/ACM Transactions on Audio, Speech, and Language Processing
- Published
- 2017-06
- Volume
- 25
- Issue
- 6
- Pages
- 1193-1206
- ISSN
- 2329-9290 (ISSN-L)
- 2329-9304
- OpenAlex
- W2581706890
- Semantic Scholar
- b8f65001205d1f632de3cf09f0cde629ec17ffcd
- Cited by
- 60
- References
- 33
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 2 · 2023: 3 · 2022: 6 · 2021: 8 · 2020: 6 · 2019: 13 · 2018: 8 · 2017: 3
- FWCI
- 6.84
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- IEEE/ACM Transactions on Audio Speech and Language Processing (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Queen Mary Research Online (Queen Mary University of London) (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- EPSRC Early Career Research Fellowship (EP/L020505/1)
- U.K. Royal Academy of Engineering Research Fellowship (RF/128)
- Max Planck Society
- 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
- animal vocalizations, scene classification, event detection, probabilistic latent component analysis, representation learning, Corvus monedula, animal behavior, free-flying birds, individual differences, temporal resolution
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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