Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Artificial Neural Networks Jamming on the Beat   This paper addresses the issue of long-scale correlations that is
characteristic for symbolic music and is a challenge for modern generative
algorithms. It suggests a very simple workaround for this challenge, namely,
generation of a drum pattern that could be further used as a foundation for
melody generation. The paper presents a large dataset of drum patterns
alongside with corresponding melodies. It explores two possible methods for
drum pattern generation. Exploring a latent space of drum patterns one could
generate new drum patterns with a given music style. Finally, the paper
demonstrates that a simple artificial neural network could be trained to
generate melodies corresponding with these drum patters used as inputs.
Resulting system could be used for end-to-end generation of symbolic music with
song-like structure and higher long-scale correlations between the notes.

34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
2
The categories of this paper are: math
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an