Quantum mechanics

Computer science

Visual arts

Musical

Physics

Artificial intelligence

Term (time)

Artificial neural network

Recurrent neural network

Melody

Speech recognition

IEEE Transactions on Cybernetics

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.

A Hierarchical Recurrent Neural Network for Symbolic Melody Generation