  Studying the vocalisations of wild animals can be a challenge due to the
limitations of traditional computational methods, which often are
time-consuming and lack reproducibility. Here, I present pykanto, a new
software package that provides a set of tools to build, manage, and explore
large sound databases. It can automatically find discrete units in animal
vocalisations, perform semi-supervised labelling of individual repertoires with
a new interactive web app, and feed data to deep learning models to study
things like individual signatures and acoustic similarity between individuals
and populations. To demonstrate its capabilities, I put the library to the test
on the vocalisations of male great tits in Wytham Woods, near Oxford, UK. The
results show that the identities of individual birds can be accurately
determined from their songs and that the use of pykanto improves the efficiency
and reproducibility of the process.
