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.
Additionally, to reproduce high-frequency components accurately, we leverage
discrete wavelet transform in the discriminators. From our experiments, Fre-GAN
achieves high-fidelity waveform generation with a gap of only 0.03 MOS compared
to ground-truth audio while outperforming standard models in quality.

本文提出了一种名为 “Fre-GAN” 的神经音源模型，通过分辨率连接生成器和分辨率智能的鉴别器，在多个频带中学习各种尺度的频谱分布，有效地实现高保真频率一致性音频合成，并在质量和 MOS 上优于标准模型。