A Hierarchical Recurrent Neural Network for Symbolic Melody Generation
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.
- Publication:
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arXiv e-prints
- Pub Date:
- December 2017
- DOI:
- 10.48550/arXiv.1712.05274
- arXiv:
- arXiv:1712.05274
- Bibcode:
- 2017arXiv171205274W
- Keywords:
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- Computer Science - Sound;
- Computer Science - Multimedia
- E-Print:
- 9 pages