FUSING SHALLOW AND DEEP LEARNING FOR BIOACOUSTIC BIRD SPECIES CLASSIFICATION

Automated c1assification of organisms to species based on their vocalizations would contribute tremendously to abilities to monitor biodiversity, with a wide range of applications in the field of ecology. In particular, automated c1assification of migrating birds' flight calls could yield new biological insights and conservation applications for birds that vocalize during migration. In this paper we explore stateof- the-art c1assification techniques for large-vocabulary bird species c1assification from flight calls. In particular, we contrast a "shallow learning" approach based on unsupervised dictionary learning with a deep convolutional neural network combined with data augmentation. We show that the two models perform comparably on a dataset of 5428 flight calls spanning 43 different species, with both significantly outperforming an MFCC baseline. Finally, we show that by combining the models using a simple late-fusion approach we can further improve the results, obtaining a state-of-the-art c1assification accuracy of 0.96.