Automatic Segmentation and Deep Learning of Bird Sounds

Abstract

We present a study on automatic birdsong recognition with deep neural networks using the BIRDCLEF2014 dataset. Through deep learning, feature hierarchies are learned that represent the data on several levels of abstraction. Deep learning has been applied with success to problems in fields such as music information retrieval and image recognition, but its use in bioacoustics is rare. Therefore, we investigate the application of a common deep learning technique (deep neural networks) in a classification task using songs from Amazonian birds. We show that various deep neural networks are capable of outperforming other classification methods. Furthermore, we present an automatic segmentation algorithm that is capable of separating bird sounds from non-bird sounds.