  With recent breakthroughs in artificial neural networks, deep generative
models have become one of the leading techniques for computational creativity.
Despite very promising progress on image and short sequence generation,
symbolic music generation remains a challenging problem since the structure of
compositions are usually complicated. In this study, we attempt to solve the
melody generation problem constrained by the given chord progression. This
music meta-creation problem can also be incorporated into a plan recognition
system with user inputs and predictive structural outputs. In particular, we
explore the effect of explicit architectural encoding of musical structure via
comparing two sequential generative models: LSTM (a type of RNN) and WaveNet
(dilated temporal-CNN). As far as we know, this is the first study of applying
WaveNet to symbolic music generation, as well as the first systematic
comparison between temporal-CNN and RNN for music generation. We conduct a
survey for evaluation in our generations and implemented Variable Markov Oracle
in music pattern discovery. Experimental results show that to encode structure
more explicitly using a stack of dilated convolution layers improved the
performance significantly, and a global encoding of underlying chord
progression into the generation procedure gains even more.
