Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Synthesis of Footsteps Sound Effects with Generative Adversarial
  Networks   Footsteps are among the most ubiquitous sound effects in multimedia
applications. There is substantial research into understanding the acoustic
features and developing synthesis models for footstep sound effects. In this
paper, we present a first attempt at adopting neural synthesis for this task.
We implemented two GAN-based architectures and compared the results with real
recordings as well as six traditional sound synthesis methods. Our
architectures reached realism scores as high as recorded samples, showing
encouraging results for the task at hand.

8
The categories of this paper are: hep-ex
14
The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
16
The categories of this paper are: stat
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org