Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Hierarchical Recurrent Neural Network for Symbolic Melody Generation   In recent years, neural networks have been used to generate symbolic
melodies. However, the long-term structure in the melody has posed great
difficulty for designing a good model. In this paper, we present a hierarchical
recurrent neural network for melody generation, which consists of three
Long-Short-Term-Memory (LSTM) subnetworks working in a coarse-to-fine manner
along time. Specifically, the three subnetworks generate bar profiles, beat
profiles and notes in turn, and the output of the high-level subnetworks are
fed into the low-level subnetworks, serving as guidance for generating the
finer time-scale melody components in low-level subnetworks. Two human behavior
experiments demonstrate the advantage of this structure over the single-layer
LSTM which attempts to learn all hidden structures in melodies. Compared with
the state-of-the-art models MidiNet and MusicVAE, the hierarchical recurrent
neural network produces better melodies evaluated by humans.

19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
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The categories of this paper are: alg-geom
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
36
The categories of this paper are: cmp-lg