ArticleeLife2022
Automated annotation of birdsong with a neural network that segments spectrograms.
Article in eLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI-Powered Vocalization Analysis in Poultry: Systematic Review of Health, Behavior, and Welfare Monitoring.Sensors (Basel, Switzerland) · 2025Pooled it
- Real-Time Segmentation and Classification of Birdsong Syllables for Learning Experiments.eNeuro · 2026Article
- TweetyBERT: Automated parsing of birdsong through self-supervised machine learning.Patterns (New York, N.Y.) · 2026Article
- Challenges and new opportunities in deciphering the meaning of corvid call sequences.Animal cognition · 2025Review
- Article
- Capturing vocal communication in a free-living corvid: high-resolution data from low-impact miniaturized tags.Animal cognition · 2025Article
- A complex social-acoustical environment supports development and maintenance in the zebra finch auditory pallium.Journal of neurophysiology · 2025Article
- Does Zipf's law of abbreviation shape birdsong?PLoS computational biology · 2025Article
- Vocal communication is seasonal in social groups of wild, free-living house mice.Proceedings. Biological sciences · 2025Article
- Comparative approaches to the neurobiology of avian vocal learning.Current opinion in neurobiology · 2025Review
- Automatic detection for bioacoustic research: a practical guide from and for biologists and computer scientists.Biological reviews of the Cambridge Philosophical Society · 2025Review
- Social context affects sequence modification learning in birdsong.Frontiers in psychology · 2025Article
- Article
- callsync: An R package for alignment and analysis of multi-microphone animal recordings.Ecology and evolution · 2024Article
- Goal-directed and flexible modulation of syllable sequence within birdsong.Nature communications · 2024Article
- SqueakOut: Autoencoder-based segmentation of mouse ultrasonic vocalizations.bioRxiv : the preprint server for biology · 2024Article
- Article
- Development of a 3D tracking system for multiple marmosets under free-moving conditions.Communications biology · 2024Article
- Deep audio embeddings for vocalisation clustering.PloS one · 2023Article
- Recent Advances at the Interface of Neuroscience and Artificial Neural Networks.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.