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arXiv · 2106.02297

Fre-GAN: Adversarial Frequency-consistent Audio Synthesis

Ji-Hoon Kim·Sang-Hoon Lee·Ji-Hyun Lee·Seong-Whan Lee
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Abstract

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.

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Ji-Hoon Kim, Sang-Hoon Lee, Ji-Hyun Lee, Seong-Whan Lee. 2021-06-04. Fre-GAN: Adversarial Frequency-consistent Audio Synthesis. https://arxiv.org/abs/2106.02297

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