  In this paper, ensembles of classifiers that exploit several data
augmentation techniques and four signal representations for training
Convolutional Neural Networks (CNNs) for audio classification are presented and
tested on three freely available audio classification datasets: i) bird calls,
ii) cat sounds, and iii) the Environmental Sound Classification dataset. The
best performing ensembles combining data augmentation techniques with different
signal representations are compared and shown to outperform the best methods
reported in the literature on these datasets. The approach proposed here
obtains state-of-the-art results in the widely used ESC-50 dataset. To the best
of our knowledge, this is the most extensive study investigating ensembles of
CNNs for audio classification. Results demonstrate not only that CNNs can be
trained for audio classification but also that their fusion using different
techniques works better than the stand-alone classifiers.
