  Deep neural networks (DNNs) have recently achieved great success in a
multitude of classification tasks. Ensembles of DNNs have been shown to improve
the performance. In this paper, we explore the recent state-of-the-art DNNs
used for image classification. We modified these DNNs and applied them to the
task of acoustic scene classification. We conducted a number of experiments on
the TUT Acoustic Scenes 2017 dataset to empirically compare these methods.
Finally, we show that the best model improves the baseline score for DCASE-2017
Task 1 by 3.1% in the test set and by 10% in the development set.
