A Hierarchical Recurrent Neural Network for Symbolic Melody Generation

By Jian Wu, Changran Hu, Yulong Wang, Xiaolin Hu, Jun Zhu. 2017

95 citations recorded by the source corpus.

In recent years, neural networks have been used to generate symbolic melodies. However, the long-term structure in the melody has posed great difficulty to design a good model. In this article, we present a hierarchical recurrent neural network (HRNN) 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 to generate the finer time-scale melody components in the 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 recently proposed models MidiNet and MusicVAE, the HRNN produces better melodies evaluated by humans.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Natural Language Processing, Generative Models, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

Ilya Sutskever

In Computer Vision and Pattern Recognition

In Natural Language Processing

In Generative Models

In Signal Processing

Citations in the corpus

Jukebox: A Generative Model for Music (2020, background): “, 2018) and HRNN (Wu et al., 2019) which use hierarchical recurrent networks, and Music Transformer (Huang et al.”