Fre-GAN: Adversarial Frequency-consistent Audio Synthesis

Understand

Although recent works on neural vocoder have improved the quality of synthesized audio, there still exists a gap between generated and ground-truth audio in frequency space.

• This difference leads to spectral artifacts such as hissing noise or reverberation, and thus degrades the sample quality.
• In this paper, we propose Fre-GAN which achieves frequency-consistent audio synthesis with highly improved generation quality.
• Specifically, we first present resolution-connected generator and resolution-wise discriminators, which help learn various scales of spectral distributions over multiple frequency bands.