Conditional WaveGAN
By C. Lee, Anoop Toffy, Gue Jun Jung, Woo-Jin Han. 2018
22 citations recorded by the source corpus.
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
Subfields: Computer Vision and Pattern Recognition, Generative Models, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Wei Ping
- Bryan Catanzaro
- Gyeongsu Chae
- Geumbyeol Hwang
- Gyuhyeon Nam
- Kexin Zhao
- Dalhyun Kim
- Jaeseong You
In Generative Models
- Wei Ping
- Zhifeng Kong
- Bryan Catanzaro
- Kexin Zhao
- Gyeongsu Chae
- Gyuhyeon Nam
- Dalhyun Kim
- Jaeseong You
In Signal Processing
- Wei Ping
- Kexin Zhao
- Zhifeng Kong
- Bryan Catanzaro
- Gyeongsu Chae
- Gyuhyeon Nam
- Dalhyun Kim
- Jaeseong You
Citations in the corpus
- DiffWave: A Versatile Diffusion Model for Audio Synthesis (2020, methodology): “We omit the comparison with conditional WaveGAN due to its noisy output audio (Lee et al., 2018).”
- GAN Vocoder: Multi-Resolution Discriminator Is All You Need (2021, background): “Early attempts [10, 11] suffered from severe upsampling artifacts.”