Identifying new bird species from differences in birdsong

The songs of birds, like human voices, are important elements of their identity. In ornithology, distinguishing the songs of different populations is as vital as identifying morphological and genetic differences. This paper takes a machine learning approach to the task. Using data gathered in Indonesia, the song from different subspecies of Black-naped Orioles and Olive-backed Sunbirds is examined. The song from different island populations is modelled with MFCCs and Gaussian Mixture Models. Analysing the performance of the classifiers on unseen test data can give an indication of song diversity. The results show that a forensic approach to birdsong analysis, inspired by speech processing, may offer invaluable insights into cryptic species diversity as well as song identification at the subspecies level.