Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adversarial Generation of Time-Frequency Features with application in
  audio synthesis   Time-frequency (TF) representations provide powerful and intuitive features
for the analysis of time series such as audio. But still, generative modeling
of audio in the TF domain is a subtle matter. Consequently, neural audio
synthesis widely relies on directly modeling the waveform and previous attempts
at unconditionally synthesizing audio from neurally generated invertible TF
features still struggle to produce audio at satisfying quality. In this
article, focusing on the short-time Fourier transform, we discuss the
challenges that arise in audio synthesis based on generated invertible TF
features and how to overcome them. We demonstrate the potential of deliberate
generative TF modeling by training a generative adversarial network (GAN) on
short-time Fourier features. We show that by applying our guidelines, our
TF-based network was able to outperform a state-of-the-art GAN generating
waveforms directly, despite the similar architecture in the two networks.

4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
21
The categories of this paper are: adap-org
2
The categories of this paper are: math
17
The categories of this paper are: q-fin
5
The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an