Topic: TweetyNet

Songbirds provide a powerful model system for studying sensory-motor learning. Many analyses require time-consuming manual annotation of the units of song, often called syllables. However, songbirds produce many more songs than can be annotated by hand, creating a bottleneck that limits the questions researchers can answer. Methods exist for automated annotation[1], but, as we show, these methods exhibit limitations when applied to songs with many syllable types and variable transitions. To address these issues, we developed an artificial neural network, TweetyNet[2], that segments and labels