TweetyNet: A neural network that enables high-throughput, automated annotation of birdsong

Songbirds provide an excellent model system for understanding sensorimotor learning. Many analyses of learning require annotating song, but songbirds produce more songs than can be annotated by hand. Existing methods for automating annotation are challenged by variable song, like that of Bengalese finches. For particularly complex song like that of canaries, no methods exist, limiting the questions researchers can investigate.

We developed an artificial neural network, TweetyNet, that automates annotation. First we benchmark the network on open datasets of Bengalese finch song, showing that TweetyNet achieves significantly lower error than a similar method, using less training data, and maintains low error across multiple days of song. We then show TweetyNet performs similarly on canary song. This accuracy allowed fully-automated analyses of datasets an order of magnitude larger than previous studies, improved the precision of statistical models of syntax, and revealed novel details of syntax in a new canary strain.

Hence TweetyNet enables automated annotation and analysis of Bengalese finch and canary song that was formerly manual.

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Recommended citation: Cohen Y, Nicholson DA, Gardner TJ (2020) “TweetyNet: A neural network that enables high-throughput, automated annotation of birdsong”. bioRxiv.