  We present a neural sequence model designed specifically for symbolic music.
The model is based on a learned edit distance mechanism which generalises a
classic recursion from computer sci- ence, leading to a neural dynamic program.
Re- peated motifs are detected by learning the transfor- mations between them.
We represent the arising computational dependencies using a novel data
structure, the edit tree; this perspective suggests natural approximations
which afford the scaling up of our otherwise cubic time algorithm. We
demonstrate our model on real and synthetic data; in all cases it out-performs
a strong stacked long short-term memory benchmark.
