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arXiv · 1712.05274

A Hierarchical Recurrent Neural Network for Symbolic Melody Generation

Jian Wu·Changran Hu·Yulong Wang·Xiaolin Hu·Jun Zhu
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Abstract

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.

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Jian Wu, Changran Hu, Yulong Wang, Xiaolin Hu, Jun Zhu. 2018-09-05. A Hierarchical Recurrent Neural Network for Symbolic Melody Generation. https://arxiv.org/abs/1712.05274

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