ArticlePloS one2014
An automated procedure for evaluating song imitation.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Article
- Striatal Dopamine at Learned Sequence Boundaries Sustains Birdsong.bioRxiv : the preprint server for biology · 2026Article
- A species rules syntax model accurately organizes birdsong syllables into songs.Scientific reports · 2026Article
- Article
- Introductory notes before zebra finch song have unique timing properties while sharing acoustic properties with song.bioRxiv : the preprint server for biology · 2025Article
- Article
- Machine learning and statistical classification of birdsong link vocal acoustic features with phylogeny.Scientific reports · 2023Article
- A researcher's guide to the comparative assessment of vocal production learning.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2021Article
- Learning in the time of COVID: insights from the zebra finch - a social vocal-learner.Current opinion in neurobiology · 2021Review
- Low-dimensional learned feature spaces quantify individual and group differences in vocal repertoires.eLife · 2021Article
- An avian cortical circuit for chunking tutor song syllables into simple vocal-motor units.Nature communications · 2020Article
- Article
- Songbird Ventral Pallidum Sends Diverse Performance Error Signals to Dopaminergic Midbrain.Neuron · 2019Article
- Vocal learning in songbirds requires cholinergic signaling in a motor cortex-like nucleus.Journal of neurophysiology · 2018Article
- An automated approach to the quantitation of vocalizations and vocal learning in the songbird.PLoS computational biology · 2018Article
- A subcortical circuit linking the cerebellum to the basal ganglia engaged in vocal learning.eLife · 2018Article
- Article
- VoICE: A semi-automated pipeline for standardizing vocal analysis across models.Scientific reports · 2015Article
- Article
- Article
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2 authors.
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Abstract
Songbirds have emerged as an excellent model system to understand the neural basis of vocal and motor learning. Like humans, songbirds learn to imitate the vocalizations of their parents or other conspecific "tutors." Young songbirds learn by comparing their own vocalizations to the memory of their tutor song, slowly improving until over the course of several weeks they can achieve an excellent imitation of the tutor. Because of the slow progression of vocal learning, and the large amounts of singing generated, automated algorithms for quantifying vocal imitation have become increasingly important for studying the mechanisms underlying this process. However, methodologies for quantifying song imitation are complicated by the highly variable songs of either juvenile birds or those that learn poorly because of experimental manipulations. Here we present a method for the evaluation of song imitation that incorporates two innovations: First, an automated procedure for selecting pupil song segments, and, second, a new algorithm, implemented in Matlab, for computing both song acoustic and sequence similarity. We tested our procedure using zebra finch song and determined a set of acoustic features for which the algorithm optimally differentiates between similar and non-similar songs.
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