Conditional WaveGAN
Computer Vision and Pattern Recognition
2018-09-30 v1 Machine Learning
Abstract
Generative models are successfully used for image synthesis in the recent years. But when it comes to other modalities like audio, text etc little progress has been made. Recent works focus on generating audio from a generative model in an unsupervised setting. We explore the possibility of using generative models conditioned on class labels. Concatenation based conditioning and conditional scaling were explored in this work with various hyper-parameter tuning methods. In this paper we introduce Conditional WaveGANs (cWaveGAN). Find our implementation at https://github.com/acheketa/cwavegan
Cite
@article{arxiv.1809.10636,
title = {Conditional WaveGAN},
author = {Chae Young Lee and Anoop Toffy and Gue Jun Jung and Woo-Jin Han},
journal= {arXiv preprint arXiv:1809.10636},
year = {2018}
}
Comments
Preprint