Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: High Resolution Time-Frequency Generation with Generative Adversarial
  Networks   Signal representation in Time-Frequency (TF) domain is valuable in many
applications including radar imaging and inverse synthetic aparture radar. TF
representation allows us to identify signal components or features in a mixed
time and frequency plane. There are several well-known tools, such as
Wigner-Ville Distribution (WVD), Short-Time Fourier Transform (STFT) and
various other variants for such a purpose. The main requirement for a TF
representation tool is to give a high-resolution view of the signal such that
the signal components or features are identifiable. A commonly used method is
the reassignment process which reduces the cross-terms by artificially moving
smoothed WVD values from their actual location to the center of the gravity for
that region. In this article, we propose a novel reassignment method using the
Conditional Generative Adversarial Network (CGAN). We train a CGAN to perform
the reassignment process. Through examples, it is shown that the method
generates high-resolution TF representations which are better than the current
reassignment methods.

25
The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
18
The categories of this paper are: econ
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys
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The categories of this paper are: q-fin
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The categories of this paper are: bayes-an
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The categories of this paper are: nlin
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat
33
The categories of this paper are: plasm-ph