Machine Learning for Bird Song Learning

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Songbirds, including familiar species like chaffinches and great tits, share an unusual ability with us: vocal learning. Like us, birds need to hear and imitate others in order to develop their vocal communication signals. Most mammal and vertebrate species cannot do this, including all other primate species apart from us. In recent years, research into the development, neurobiology, and genetics of song learning have revealed ever deeper links between human speech and bird song - so much so that bird song currently represents the best animal model we have for understanding the biology of speech. In order to study bird song, researchers need to accurately measure how different songs are from each other. These measures are needed to assess whether one bird really did imitate another, and how precisely they did so. Developing computer algorithms to make such measurements is difficult, however, for many of the same reasons that speech recognition is a difficult task for computers. In this grant, we will use a new approach to solve this problem - inspired by developments in speech recognition. First we will train birds to peck on buttons to get a food reward from a bird feeder, and then train them further to discriminate between different "notes" within bird songs. Then we will train "machine learning" computer algorithms to replicate the birds' decisions. We will thus develop a computer algorithm that we can use to compare bird songs in a way that is biologically validated.We will then use our algorithm to investigate how birds learn their songs. To do this, we will make use of data-sets where researchers have simply recorded the different songs sung by birds within the population. This data contains a signature of how the birds actually learned their songs in much the same way that our genomes contain signatures of our evolutionary history. We will exploit this by using a statistical technique in combination with simulation models to infer how birds learn their songs: how frequently they generate new song types due to errors or innovations; who they prefer to learn from; and which songs they prefer to learn. We will do this for 15 different species and populations, allowing us to compare how different groups learn their songs for the first time.