  The reverberation time (T60) and the direct-to-reverberant ratio (DRR) are
commonly used to characterize room acoustic environments. Both parameters can
be measured from an acoustic impulse response (AIR) or using blind estimation
methods that perform estimation directly from speech. When neural networks are
used for blind estimation, however, a large realistic dataset is needed, which
is expensive and time consuming to collect. To address this, we propose an AIR
augmentation method that can parametrically control the T60 and DRR, allowing
us to expand a small dataset of real AIRs into a balanced dataset orders of
magnitude larger. Using this method, we train a previously proposed
convolutional neural network (CNN) and show we can outperform past
single-channel state-of-the-art methods. We then propose a more efficient,
straightforward baseline CNN that is 4-5x faster, which provides an additional
improvement and is better or comparable to all previously reported single- and
multi-channel state-of-the-art methods.
