ArticleeNeuro2026
Real-Time Segmentation and Classification of Birdsong Syllables for Learning Experiments.
Article in eNeuro, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
Songbirds are essential for studying neuronal mechanisms of learned vocalizations. Closed-loop interventions require online recognition of a specific target syllable while the bird is singing, for example, for manipulation of auditory feedback, song-triggered neuronal microstimulation or optogenetics. Existing tools for closed-loop interventions can recognize only single syllables through manually created templates, with limited flexibility to adapt to new experiments. We here present Moove (Marking Online using only the Onsets of Vocal Elements), a novel neural network approach to real-time syllable segmentation and classification of Bengalese finch songs. Moove's two-stage architecture detects syllable onsets and offsets and classifies syllables using acoustic information only from the first part of the syllable, enabling precise temporal contingency between behavior and feedback. We verify Moove's fast and accurate online annotation of all recorded syllables in five adult male Bengalese finches (
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