Acoustic detection of unknown bird species and individuals

Computational bioacoustics is a relatively young research area,yet it has increasingly received attention over the last decade because it can be used in a wide range of appli-cations in a cost-effective manner.This work focuses on the problem of detecting the novel bird calls and songs associated with various species and individual birds.To this end,variational autoencoders,consisting of deep encoding-decoding networks,are employed.The encoder encompasses a series of convolutional layers leading to a smooth high-level abstraction of log-Mel spectrograms that characterise bird vocalisations.The decoder operates on this latent representation to generate each respective original observation.Novel species/individual detection is carried out by monitoring and thresholding the expected reconstruction probability.We thoroughly evaluate the pro-posed method on two different data sets,including the vocalisations of 11 North American bird species and 16 Athene noctua individuals.