- Increases in the scale and complexity of behavioral data pose an increasing challenge for data analysis. A common strategy involves replacing entire behaviors with small numbers of handpicked, domain-specific features, but this approach suffers from several crucial limitations. For example, handpicked features may miss important dimensions of variability, and correlations among them complicate statistical testing. Here, by contrast, we apply the variational autoencoder (VAE), an unsupervised learning method, to learn features directly from data and quantify the vocal behavior of two model species: the laboratory mouse and the zebra finch. The VAE converges on a parsimonious representation that outperforms handpicked features on a variety of common ... [Read More]
- Total Size
- 59 files (44.4 GB)
- Data Citation
- Goffinet, J., Brudner, S., Mooney, R., & Pearson, J. (2021). Data from: Low-dimensional learned feature spaces quantify individual and group differences in vocal repertoires. Duke Research Data Repository. https://doi.org/10.7924/r4gq6zn8w
- DOI
- 10.7924/r4gq6zn8w
- Publication Date
- May 24, 2021
- ARK
- ark:/87924/r4gq6zn8w
- Contributor
- Affiliation
- Publisher
- Type
- Format
- Related Materials
- Contact
- Jack Gofffinet: jack.goffinet@duke.edu, ORCID: 0000-0001-6729-0848
- Title
- Data from: Low-dimensional learned feature spaces quantify individual and group differences in vocal repertoires
- Repository
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