ArticlePloS one2017
A fast and accurate zebra finch syllable detector.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Real-Time Segmentation and Classification of Birdsong Syllables for Learning Experiments.eNeuro · 2026Article
- BattyCoda: A novel open-source software for bat call annotation and classification.Ecological informatics · 2025Article
- A system for controlling vocal communication networks.Scientific reports · 2021Article
- Quantifying behavior to understand the brain.Nature neuroscience · 2020Review
- Finding, visualizing, and quantifying latent structure across diverse animal vocal repertoires.PLoS computational biology · 2020Article
- Article
- An open source, wireless capable miniature microscope system.Journal of neural engineering · 2017Article
Corrections and comments
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Authors and funding
4 authors.
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Abstract
The song of the adult male zebra finch is strikingly stereotyped. Efforts to understand motor output, pattern generation, and learning have taken advantage of this consistency by investigating the bird's ability to modify specific parts of song under external cues, and by examining timing relationships between neural activity and vocal output. Such experiments require that precise moments during song be identified in real time as the bird sings. Various syllable-detection methods exist, but many require special hardware, software, and know-how, and details on their implementation and performance are scarce. We present an accurate, versatile, and fast syllable detector that can control hardware at precisely timed moments during zebra finch song. Many moments during song can be isolated and detected with false negative and false positive rates well under 1% and 0.005% respectively. The detector can run on a stock Mac Mini with triggering delay of less than a millisecond and a jitter of σ ≈ 2 milliseconds.
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