A dynamical system as the source of augmentation in a deep learning problem

In this work we build a convolutional neural network capable of identifying individual birds by their songs. Since the actual data available from each individual is very limited, we use a dynamical system capable of synthesizing realistic songs, to generate surrogate-training data. The different synthetic songs are the result of integrating the dynamical system with slightly varied parameters. We show that a data set built in this way allows us to train the network to successfully identify the different individuals in our study. In this way, we present a novel way to perform data augmentation using dynamical systems.