A Multi-Discriminator CycleGAN for Unsupervised Non-Parallel Instrumental Music Conversion

• Main Authors: Jie-Hong Lin, 林杰鴻
• Other Authors: 吳家麟
• Format: Others
• Language: en_US
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/pprkqp

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 106 === A trainable instrumental artist can easily give an interesting domain translation performance, such as a violin player can cover Mozart’s Rondo Alla Turca or gently perform the well-known piano song, fur elise from Beethoven. Given the success of deep neural network in image processing, the generative adversarial network based model is broadly used for domain transfer problem. In this thesis, we present a light-weight generative model based on cycle-consistent adversarial network (CycleGAN) for instrumental music conversion. The proposed model employs multiple discriminator that focuses on fine-grained local details of the frequency features. We also evaluate the original CycleGAN model and the multiple independent discriminator based CycleGAN model on the MagnagTagATune dataset. As a result, we have 1) a reliable discriminator that reduces the number of parameter and 2) a better generator that is able to transfer the characteristics between different types of musical instrument and generate more natural domain specific instrumental music.