Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Impulse Response Data Augmentation and Deep Neural Networks for Blind
  Room Acoustic Parameter Estimation   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.

9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex