Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Classical Music Prediction and Composition by means of Variational
  Autoencoders   This paper proposes a new model for music prediction based on Variational
Autoencoders (VAEs). In this work, VAEs are used in a novel way in order to
address two different problems: music representation into the latent space, and
using this representation to make predictions of the future values of the
musical piece. This approach was trained with different songs of a classical
composer. As a result, the system can represent the music in the latent space,
and make accurate predictions. Therefore, the system can be used to compose new
music either from an existing piece or from a random starting point. An
additional feature of this system is that a small dataset was used for
training. However, results show that the system is able to return accurate
representations and predictions in unseen data.

30
The categories of this paper are: q-alg
16
The categories of this paper are: stat
24
The categories of this paper are: dg-ga
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
3
The categories of this paper are: physics