  The classification of acoustic environments allows for machines to better
understand the auditory world around them. The use of deep learning in order to
teach machines to discriminate between different rooms is a new area of
research. Similarly to other learning tasks, this task suffers from the
high-dimensionality and the limited availability of training data. Data
augmentation methods have proven useful in addressing this issue in the tasks
of sound event detection and scene classification. This paper proposes a method
for data augmentation for the task of room classification from reverberant
speech. Generative Adversarial Networks (GANs) are trained that generate
artificial data as if they were measured in real rooms. This provides
additional training examples to the classifiers without the need for any
additional data collection, which is time-consuming and often impractical. A
representation of acoustic environments is proposed, which is used to train the
GANs. The representation is based on a sparse model for the early reflections,
a stochastic model for the reverberant tail and a mixing mechanism between the
two. In the experiments shown, the proposed data augmentation method increases
the test accuracy of a CNN-RNN room classifier from 89.4% to 95.5%.
