  Automatic melody generation has been a long-time aspiration for both AI
researchers and musicians. However, learning to generate euphonious melodies
has turned out to be highly challenging. This paper introduces 1) a new variant
of variational autoencoder (VAE), where the model structure is designed in a
modularized manner in order to model polyphonic and dynamic music with domain
knowledge, and 2) a hierarchical encoding/decoding strategy, which explicitly
models the dependency between melodic features. The proposed framework is
capable of generating distinct melodies that sounds natural, and the
experiments for evaluating generated music clips show that the proposed model
outperforms the baselines in human evaluation.
