  A common approach to generating symbolic music using neural networks involves
repeated sampling of an autoregressive model until the full output sequence is
obtained. While such approaches have shown some promise in generating short
sequences of music, this typically has not extended to cases where the final
target sequence is significantly longer, for example an entire piece of music.
In this work we propose a network trained in an adversarial process to generate
entire pieces of solo shakuhachi music, in the form of symbolic notation. The
pieces are intended to refer clearly to traditional shakuhachi music,
maintaining idiomaticity and key aesthetic qualities, while also adding novel
features, ultimately creating worthy additions to the contemporary shakuhachi
repertoire. A key subproblem is also addressed, namely the lack of relevant
training data readily available, in two steps: firstly, we introduce the
PH_Shaku dataset for symbolic traditional shakuhachi music; secondly, we build
on previous work using conditioning in generative adversarial networks to
introduce a technique for data augmentation.
