  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.
