Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Audio style transfer   'Style transfer' among images has recently emerged as a very active research
topic, fuelled by the power of convolution neural networks (CNNs), and has
become fast a very popular technology in social media. This paper investigates
the analogous problem in the audio domain: How to transfer the style of a
reference audio signal to a target audio content? We propose a flexible
framework for the task, which uses a sound texture model to extract statistics
characterizing the reference audio style, followed by an optimization-based
audio texture synthesis to modify the target content. In contrast to mainstream
optimization-based visual transfer method, the proposed process is initialized
by the target content instead of random noise and the optimized loss is only
about texture, not structure. These differences proved key for audio style
transfer in our experiments. In order to extract features of interest, we
investigate different architectures, whether pre-trained on other tasks, as
done in image style transfer, or engineered based on the human auditory system.
Experimental results on different types of audio signal confirm the potential
of the proposed approach.

10
The categories of this paper are: q-bio
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci