Conditional WaveGAN
This work addresses audio synthesis for applications requiring labeled data, but it appears incremental as it adapts existing methods to a new modality.
The paper tackles the problem of generating audio using generative models conditioned on class labels, introducing Conditional WaveGANs (cWaveGAN) and exploring concatenation-based conditioning and conditional scaling with hyper-parameter tuning.
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