Automated inference of respiratory and syringeal biomechanical trajectories from birdsong acoustics
8 affiliations
- 1Neurosciences Graduate Program, University of California San Diego, La Jolla, CA, USA
- 2Medical Scientist Training Program, University of California San Diego, La Jolla, CA, USA
- 3Department of Physics and Astronomy, Minnesota State University-Mankato, Mankato, MN, USA
- 4Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
- 5Department of Psychology, Texas Christian University, Fort Worth, TX, USA
- 6Department of Psychology, University of California San Diego, La Jolla, CA, USA
- 7Neurobiology Section, Division of Biological Sciences, University of California San Diego, La Jolla, CA, USA
- 8Kavli Institute for Brain and Mind, University of California San Diego, La Jolla, CA, USA
Abstract
Songbirds, in particular zebra finches ( Taeniopygia guttata ), provide a powerful model for investigating the neural mechanisms of learned vocal behavior. Researchers typically rely on the acoustic structure of birdsong to quantify vocal behavior. As a more direct measure of motor control, we present VIBE: Vocal acoustic Inversion to Biomechanical Estimates, an open-source pipeline that recovers the biomechanical control parameters of song production directly from the acoustic waveform. Biomechanical models of the songbird syrinx describe vocal production with two continuously varying parameters: α and β , representing subsyringeal air sac pressure and syringeal muscle tension, respectively. Recovering these parameters from song acoustics provides a motor-based coordinate system against which neural activity or other dependent variables can be directly compared. Because α and β are the coupled control parameters of a nonlinear oscillator, their joint recovery is non-trivial. VIBE addresses this through iterative optimization of the governing normal-form equations. We validate VIBE against recorded air sac pressure across 44 songs from twelve birds, showing that the recovered α corresponds to empirically measured air sac pressure. Pairing VIBE with Neuropixels recordings from RA in five birds, we find that RA activity is well predicted by the recovered parameters, and that α and β add predictive power beyond the acoustic features of song. By recovering biomechanical control parameters from the acoustic signal, VIBE makes the biomechanical coordinate system of song production accessible to the broader songbird research community. New & Noteworthy VIBE provides a novel, fully automated pipeline to recover the biomechanical control parameters of the avian vocal organ, α and β , as continuously varying quantities from the raw acoustic waveform, making the full biomechanical model of song production accessible at the scale of modern datasets.
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BibTeX
@article{ostrowski2026automated,
title = {{Automated inference of respiratory and syringeal biomechanical trajectories from birdsong acoustics}},
author = {Lauren M. Ostrowski and Jorge M. Méndez and Pablo Tostado-Marcos and Brenton G. Cooper and Timothy Q. Gentner},
journal = {Journal of neurophysiology},
year = {2026},
month = oct,
doi = {10.1152/jn.90042.2026},
url = {https://doi.org/10.1152/jn.90042.2026},
note = {CC BY},
}
RIS
TY - JOUR
TI - Automated inference of respiratory and syringeal biomechanical trajectories from birdsong acoustics
AU - Lauren M. Ostrowski
AU - Jorge M. Méndez
AU - Pablo Tostado-Marcos
AU - Brenton G. Cooper
AU - Timothy Q. Gentner
PY - 2026
DA - 2026/10/01
JO - Journal of neurophysiology
DO - 10.1152/jn.90042.2026
UR - https://doi.org/10.1152/jn.90042.2026
AB - Songbirds, in particular zebra finches ( Taeniopygia guttata ), provide a powerful model for investigating the neural mechanisms of learned vocal behavior. Researchers typically rely on the acoustic structure of birdsong to quantify vocal behavior. As a more direct measure of motor control, we present VIBE: Vocal acoustic Inversion to Biomechanical Estimates, an open-source pipeline that recovers the biomechanical control parameters of song production directly from the acoustic waveform. Biomechanical models of the songbird syrinx describe vocal production with two continuously varying parameters: α and β , representing subsyringeal air sac pressure and syringeal muscle tension, respectively. Recovering these parameters from song acoustics provides a motor-based coordinate system against which neural activity or other dependent variables can be directly compared. Because α and β are the coupled control parameters of a nonlinear oscillator, their joint recovery is non-trivial. VIBE addresses this through iterative optimization of the governing normal-form equations. We validate VIBE against recorded air sac pressure across 44 songs from twelve birds, showing that the recovered α corresponds to empirically measured air sac pressure. Pairing VIBE with Neuropixels recordings from RA in five birds, we find that RA activity is well predicted by the recovered parameters, and that α and β add predictive power beyond the acoustic features of song. By recovering biomechanical control parameters from the acoustic signal, VIBE makes the biomechanical coordinate system of song production accessible to the broader songbird research community. New & Noteworthy VIBE provides a novel, fully automated pipeline to recover the biomechanical control parameters of the avian vocal organ, α and β , as continuously varying quantities from the raw acoustic waveform, making the full biomechanical model of song production accessible at the scale of modern datasets.
SN - 0022-3077
SN - 1522-1598
ER -