Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Conditioning Deep Generative Raw Audio Models for Structured Automatic
  Music   Existing automatic music generation approaches that feature deep learning can
be broadly classified into two types: raw audio models and symbolic models.
Symbolic models, which train and generate at the note level, are currently the
more prevalent approach; these models can capture long-range dependencies of
melodic structure, but fail to grasp the nuances and richness of raw audio
generations. Raw audio models, such as DeepMind's WaveNet, train directly on
sampled audio waveforms, allowing them to produce realistic-sounding, albeit
unstructured music. In this paper, we propose an automatic music generation
methodology combining both of these approaches to create structured,
realistic-sounding compositions. We consider a Long Short Term Memory network
to learn the melodic structure of different styles of music, and then use the
unique symbolic generations from this model as a conditioning input to a
WaveNet-based raw audio generator, creating a model for automatic, novel music.
We then evaluate this approach by showcasing results of this work.

20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph