Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: NU-GAN: High resolution neural upsampling with GAN   In this paper, we propose NU-GAN, a new method for resampling audio from
lower to higher sampling rates (upsampling). Audio upsampling is an important
problem since productionizing generative speech technology requires operating
at high sampling rates. Such applications use audio at a resolution of 44.1 kHz
or 48 kHz, whereas current speech synthesis methods are equipped to handle a
maximum of 24 kHz resolution. NU-GAN takes a leap towards solving audio
upsampling as a separate component in the text-to-speech (TTS) pipeline by
leveraging techniques for audio generation using GANs. ABX preference tests
indicate that our NU-GAN resampler is capable of resampling 22 kHz to 44.1 kHz
audio that is distinguishable from original audio only 7.4% higher than random
chance for single speaker dataset, and 10.8% higher than chance for
multi-speaker dataset.

8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
18
The categories of this paper are: econ
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas