GANkyoku: a Generative Adversarial Network for Shakuhachi Music
This work addresses a domain-specific problem for music generation, particularly in traditional shakuhachi music, by introducing a new dataset and method, but it is incremental as it builds on existing GAN techniques with conditioning for data augmentation.
The authors tackled the problem of generating long sequences of symbolic music, specifically entire pieces of solo shakuhachi music, by proposing a GAN trained adversarially, which created pieces that maintain traditional idiomaticity while adding novel features to enrich the contemporary repertoire.
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.