GANkyoku: a Generative Adversarial Network for Shakuhachi Music

TL;DR

This paper introduces GANkyoku, a GAN-based model for generating full-length traditional shakuhachi music pieces in symbolic notation, addressing data scarcity and maintaining musical idiomaticity.

Contribution

It presents a novel GAN framework for long-form shakuhachi music generation and introduces the PH_Shaku dataset along with a data augmentation technique.

Findings

Successfully generated entire shakuhachi pieces with traditional qualities

Created the PH_Shaku dataset for training data

Developed a data augmentation method for limited datasets

Abstract

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.