Automated annotation of birdsong with a neural network that segments spectrograms
- Weizmann Institute of Science, Israel
- Emory University, United States
- Boston University, United States
- University of Oregon, United States
Abstract
Songbirds provide a powerful model system for studying sensory-motor learning. However, many analyses of birdsong require time-consuming, manual annotation of its elements, called syllables. Automated methods for annotation have been proposed, but these methods assume that audio can be cleanly segmented into syllables, or they require carefully tuning multiple statistical models. Here we present TweetyNet: a single neural network model that learns how to segment spectrograms of birdsong into annotated syllables. We show that TweetyNet mitigates limitations of methods that rely on segmented audio. We also show that TweetyNet performs well across multiple individuals from two species of songbirds, Bengalese finches and canaries. Lastly, we demonstrate that using TweetyNet we can accurately annotate very large datasets containing multiple days of song, and that these predicted annotations replicate key findings from behavioral studies. In addition, we provide open-source software to assist other researchers, and a large dataset of annotated canary song that can serve as a benchmark. We conclude that TweetyNet makes it possible to address a wide range of new questions about birdsong.
Data availability
Datasets of annotated Bengalese finch song are available at:https://figshare.com/articles/Bengalese_Finch_song_repository/4805749https://figshare.com/articles/BirdsongRecognition/3470165Datasets of annotated canary song are available at:https://doi.org/10.5061/dryad.xgxd254f4Model checkpoints, logs, and source data files are available at:http://dx.doi.org/10.5061/dryad.gtht76hk4Source data files for figure are in the repository associated with the paper:https://github.com/yardencsGitHub/tweetynet(version 0.4.3, 10.5281/zenodo.3978389).
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Song recordings and annotation files of 3 canaries used to evaluate training of TweetyNet models for birdsong segmentation and annotationDryad Digital Repository, doi:10.5061/dryad.xgxd254f4.
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Model checkpoints, logs, and source data filesDryad Digital Repository, doi:10.5061/dryad.gtht76hk4.
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Bengalese Finch song repository.Figshare, https://doi.org/10.6084/m9.figshare.4805749.v6.
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BirdsongRecognition.Figshare, https://doi.org/10.6084/m9.figshare.3470165.v1.
Article and author information
Author details
Funding
National Institute of Neurological Disorders and Stroke (R01NS104925)
- Alexa Sanchioni
- Emily K Mallaber
- Viktoriya Skidanova
- Timothy J Gardner
National Institute of Neurological Disorders and Stroke (R24NS098536)
- Alexa Sanchioni
- Emily K Mallaber
- Viktoriya Skidanova
- Timothy J Gardner
National Institute of Neurological Disorders and Stroke (R01NS118424)
- Timothy J Gardner
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: All procedures were approved by the Institutional Animal Care and Use Committees of Boston University (protocol numbers 14-028 and 14-029). Song data were collected from adult male canaries (n = 5). Canaries were individually housed for the entire duration of the experiment and kept on a light-dark cycle matching the daylight cycle in Boston (42.3601 N). The birds were not used in any other experiments.
Copyright
© 2022, Cohen et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Automated annotation of birdsong with a neural network that segments spectrograms
eLife 11:e63853.
https://doi.org/10.7554/eLife.63853
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