[2K4-ES-2] Machine learning: GAN

[2K4-ES-2-01] Transfer symbolic music style from latent representation

〇Yingfeng Fu¹, Yusuke Tanimura^2,1, Hidemoto Nakada^2,1 (1. The University of Tsukuba, 2. AIST)

Keywords:GAN, CycleGAN, Style transfer

Generative models has been widely applied in many computer vision scenarios. Two series of models, GenerativeAdversarial Network(GAN) and Variational Autoencoder(VAE), are getting more and more popular in represen-tation learning. Training these model on discrete sequence data generation is still challenging. We want to takeadvantage of both kind of models. In this work, we first improved a CycleGAN based model to transfer MIDI musicgenre. Then we want to find to combine the CycleGAN model together with a disentangled latent representationfrom VAE to have better understanding of music style.