Singing Style Transfer Using Cycle-Consistent Boundary Equilibrium Generative Adversarial Networks

Abstract

Can we make a famous rap singer like Eminem sing whatever our favorite song? Singing style transfer attempts to make this possible, by replacing the vocal of a song from the source singer to the target singer. This paper presents a method that learns from unpaired data for singing style transfer using generative adversarial networks.

arXiv comment3 pages, 3 figures, demo website: http://mirlab.org/users/haley.wu/cybegan

References in corpus (3)

• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
• BEGAN: Boundary Equilibrium Generative Adversarial Networks
• Denoising Auto-encoder with Recurrent Skip Connections and Residual Regression for Music Source Separation

Cited by in corpus (5)

• Crossing You in Style: Cross-modal Style Transfer from Music to Visual Arts
• Speech-to-Singing Conversion in an Encoder-Decoder Framework
• Play as You Like: Timbre-enhanced Multi-modal Music Style Transfer
• Speech-to-Singing Conversion based on Boundary Equilibrium GAN
• PerformanceNet: Score-to-Audio Music Generation with Multi-Band Convolutional Residual Network