Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Personalized Popular Music Generation Using Imitation and Structure   Many practices have been presented in music generation recently. While
stylistic music generation using deep learning techniques has became the main
stream, these models still struggle to generate music with high musicality,
different levels of music structure, and controllability. In addition, more
application scenarios such as music therapy require imitating more specific
musical styles from a few given music examples, rather than capturing the
overall genre style of a large data corpus. To address requirements that
challenge current deep learning methods, we propose a statistical machine
learning model that is able to capture and imitate the structure, melody,
chord, and bass style from a given example seed song. An evaluation using 10
pop songs shows that our new representations and methods are able to create
high-quality stylistic music that is similar to a given input song. We also
discuss potential uses of our approach in music evaluation and music therapy.

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The categories of this paper are: dg-ga
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The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
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The categories of this paper are: solv-int
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The categories of this paper are: stat
34
The categories of this paper are: chem-ph
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The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
9
The categories of this paper are: nlin
21
The categories of this paper are: adap-org