2 citations · 2 across the 2 of their papers we have counts for
3 papers
cs.SD2021★ 2 cited
DarkGAN: Exploiting Knowledge Distillation for Comprehensible Audio Synthesis with GANs
Javier Nistal, Stefan Lattner, Gaël Richard
Generative Adversarial Networks (GANs) have achieved excellent audio synthesis quality in the last years. However, making them operable with semantically meaningful controls remain…
cs.SD2021
VQCPC-GAN: Variable-Length Adversarial Audio Synthesis Using Vector-Quantized Contrastive Predictive Coding
Javier Nistal, Cyran Aouameur, Stefan Lattner +1
Influenced by the field of Computer Vision, Generative Adversarial Networks (GANs) are often adopted for the audio domain using fixed-size two-dimensional spectrogram representatio…
eess.AS2020
Comparing Representations for Audio Synthesis Using Generative Adversarial Networks
Javier Nistal, Stefan Lattner, Gaël Richard
In this paper, we compare different audio signal representations, including the raw audio waveform and a variety of time-frequency representations, for the task of audio synthesis…