1. Neuroscience

Automated annotation of birdsong with a neural network that segments spectrograms

  1. Yarden Cohen  Is a corresponding author
  2. David Aaron Nicholson
  3. Alexa Sanchioni
  4. Emily K Mallaber
  5. Viktoriya Skidanova
  6. Timothy J Gardner  Is a corresponding author
  1. Weizmann Institute of Science, Israel
  2. Emory University, United States
  3. Boston University, United States
  4. University of Oregon, United States
https://doi.org/10.7554/eLife.63853
Share this article
Cite this article
  1. Yarden Cohen
  2. David Aaron Nicholson
  3. Alexa Sanchioni
  4. Emily K Mallaber
  5. Viktoriya Skidanova
  6. Timothy J Gardner
(2022)
Automated annotation of birdsong with a neural network that segments spectrograms
eLife 11:e63853.
https://doi.org/10.7554/eLife.63853
  2. Download BibTeX
  3. Download .RIS

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).

The following data sets were generated
The following previously published data sets were used
    1. Koumura
    2. Takuya
    (2016) BirdsongRecognition.
    Figshare, https://doi.org/10.6084/m9.figshare.3470165.v1.
    https://figshare.com/articles/media/BirdsongRecognition/3470165

Article and author information

Author details

  1. Yarden Cohen

    Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel
    For correspondence
    yarden.j.cohen@weizmann.ac.il
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8149-6954

  2. David Aaron Nicholson

    Department of Biology, Emory University, Atlanta, United States
    Competing interests
    The authors declare that no competing interests exist.

  3. Alexa Sanchioni

    Department of Biology, Boston University, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.

  4. Emily K Mallaber

    Department of Biology, Boston University, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.

  5. Viktoriya Skidanova

    Department of Biology, Boston University, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.

  6. Timothy J Gardner

    Phil and Penny Knight Campus for Accelerating Scientific Impact, University of Oregon, Eugene, United States
    For correspondence
    timg@uoregon.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1744-3970

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.

Metrics

  • 4,625
    views
  • 511
    downloads
  • 49
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Yarden Cohen
  2. David Aaron Nicholson
  3. Alexa Sanchioni
  4. Emily K Mallaber
  5. Viktoriya Skidanova
  6. Timothy J Gardner
(2022)
Automated annotation of birdsong with a neural network that segments spectrograms
eLife 11:e63853.
https://doi.org/10.7554/eLife.63853

Share this article

https://doi.org/10.7554/eLife.63853

Categories and tags

Further reading

    1. Neuroscience

    Visual routines for detecting causal interactions are tuned to motion direction

    Sven Ohl, Martin Rolfs
    Research Article

    Detecting causal relations structures our perception of events in the world. Here, we determined for visual interactions whether generalized (i.e. feature-invariant) or specialized (i.e. feature-selective) visual routines underlie the perception of causality. To this end, we applied a visual adaptation protocol to assess the adaptability of specific features in classical launching events of simple geometric shapes. We asked observers to report whether they observed a launch or a pass in ambiguous test events (i.e. the overlap between two discs varied from trial to trial). After prolonged exposure to causal launch events (the adaptor) defined by a particular set of features (i.e. a particular motion direction, motion speed, or feature conjunction), observers were less likely to see causal launches in subsequent ambiguous test events than before adaptation. Crucially, adaptation was contingent on the causal impression in launches as demonstrated by a lack of adaptation in non-causal control events. We assessed whether this negative aftereffect transfers to test events with a new set of feature values that were not presented during adaptation. Processing in specialized (as opposed to generalized) visual routines predicts that the transfer of visual adaptation depends on the feature similarity of the adaptor and the test event. We show that the negative aftereffects do not transfer to unadapted launch directions but do transfer to launch events of different speeds. Finally, we used colored discs to assign distinct feature-based identities to the launching and the launched stimulus. We found that the adaptation transferred across colors if the test event had the same motion direction as the adaptor. In summary, visual adaptation allowed us to carve out a visual feature space underlying the perception of causality and revealed specialized visual routines that are tuned to a launch’s motion direction.

    1. Neuroscience

    Serotonin modulates infraslow oscillation in the dentate gyrus during non-REM sleep

    Gergely F Turi, Sasa Teng ... Yueqing Peng
    Research Article

    Synchronous neuronal activity is organized into neuronal oscillations with various frequency and time domains across different brain areas and brain states. For example, hippocampal theta, gamma, and sharp wave oscillations are critical for memory formation and communication between hippocampal subareas and the cortex. In this study, we investigated the neuronal activity of the dentate gyrus (DG) with optical imaging tools during sleep-wake cycles in mice. We found that the activity of major glutamatergic cell populations in the DG is organized into infraslow oscillations (0.01–0.03 Hz) during NREM sleep. Although the DG is considered a sparsely active network during wakefulness, we found that 50% of granule cells and about 25% of mossy cells exhibit increased activity during NREM sleep, compared to that during wakefulness. Further experiments revealed that the infraslow oscillation in the DG was correlated with rhythmic serotonin release during sleep, which oscillates at the same frequency but in an opposite phase. Genetic manipulation of 5-HT receptors revealed that this neuromodulatory regulation is mediated by Htr1a receptors and the knockdown of these receptors leads to memory impairment. Together, our results provide novel mechanistic insights into how the 5-HT system can influence hippocampal activity patterns during sleep.

    1. Neuroscience

    Synaptic deregulation of cholinergic projection neurons causes olfactory dysfunction across five fly Parkinsonism models

    Ulrike Pech, Jasper Janssens ... Patrik Verstreken
    Research Article

    The classical diagnosis of Parkinsonism is based on motor symptoms that are the consequence of nigrostriatal pathway dysfunction and reduced dopaminergic output. However, a decade prior to the emergence of motor issues, patients frequently experience non-motor symptoms, such as a reduced sense of smell (hyposmia). The cellular and molecular bases for these early defects remain enigmatic. To explore this, we developed a new collection of five fruit fly models of familial Parkinsonism and conducted single-cell RNA sequencing on young brains of these models. Interestingly, cholinergic projection neurons are the most vulnerable cells, and genes associated with presynaptic function are the most deregulated. Additional single nucleus sequencing of three specific brain regions of Parkinson’s disease patients confirms these findings. Indeed, the disturbances lead to early synaptic dysfunction, notably affecting cholinergic olfactory projection neurons crucial for olfactory function in flies. Correcting these defects specifically in olfactory cholinergic interneurons in flies or inducing cholinergic signaling in Parkinson mutant human induced dopaminergic neurons in vitro using nicotine, both rescue age-dependent dopaminergic neuron decline. Hence, our research uncovers that one of the earliest indicators of disease in five different models of familial Parkinsonism is synaptic dysfunction in higher-order cholinergic projection neurons and this contributes to the development of hyposmia. Furthermore, the shared pathways of synaptic failure in these cholinergic neurons ultimately contribute to dopaminergic dysfunction later in life.