  In this paper we present ensembles of classifiers for automated animal audio
classification, exploiting different data augmentation techniques for training
Convolutional Neural Networks (CNNs). The specific animal audio classification
problems are i) birds and ii) cat sounds, whose datasets are freely available.
We train five different CNNs on the original datasets and on their versions
augmented by four augmentation protocols, working on the raw audio signals or
their representations as spectrograms. We compared our best approaches with the
state of the art, showing that we obtain the best recognition rate on the same
datasets, without ad hoc parameter optimization. Our study shows that different
CNNs can be trained for the purpose of animal audio classification and that
their fusion works better than the stand-alone classifiers. To the best of our
knowledge this is the largest study on data augmentation for CNNs in animal
audio classification audio datasets using the same set of classifiers and
parameters. Our MATLAB code is available at https://github.com/LorisNanni.
