A new machine learning model, TweetyBERT, automatically segments and classifies canary vocalizations with expert-level accuracy, offering a scalable platform for neuroscience, providing insights into the neural basis of how the brain learns and produces language, and offering potential applications for understanding animal vocalization more broadly. The study by University of Oregon researchers appears in the journal Patterns.
“Current AI methods for analyzing animal vocalizations require human-labeled training data, a slow and labor-intensive process. We developed TweetyBERT, a self-supervised neural network for analyzing birdsongs. It can rapidly process unlabeled vocal recordings, identify communication units, and annotate sequences,” says Tim Gardner, associate professor of bioengineering at the University of Oregon’s Knight Campus.
Neuroscientists use canaries, or songbirds, because of their remarkable ability to learn complex and lengthy songs throughout their lives, providing a window into the neural basis of complex learned behaviors…