Deep generative models have achieved significant progress in speech synthesis
to date, while high-fidelity singing voice synthesis is still an open problem
for its long continuous pronunciation, rich high-frequency parts, and strong
expressiveness. Existing neural vocoders designed for text-to-speech cannot
directly be applied to singing voice synthesis because they result in glitches
and poor high-frequency reconstruction. In this work, we propose SingGAN, a
generative adversarial network designed for high-fidelity singing voice
synthesis. Specifically, 1) to alleviate the glitch problem in the generated
samples, we propose source excitation with the adaptive feature learning
filters to expand the receptive field patterns and stabilize long continuous
signal generation; and 2) SingGAN introduces global and local discriminators at
different scales to enrich low-frequency details and promote high-frequency
reconstruction; and 3) To improve the training efficiency, SingGAN includes
auxiliary spectrogram losses and sub-band feature matching penalty loss. To the
best of our knowledge, SingGAN is the first work designed toward high-fidelity
singing voice vocoding. Our evaluation of SingGAN demonstrates the
state-of-the-art results with higher-quality (MOS 4.05) samples. Also, SingGAN
enables a sample speed of 50x faster than real-time on a single NVIDIA 2080Ti
GPU. We further show that SingGAN generalizes well to the mel-spectrogram
inversion of unseen singers, and the end-to-end singing voice synthesis system
SingGAN-SVS enjoys a two-stage pipeline to transform the music scores into
expressive singing voices. Audio samples are available at
https://SingGAN.github.io/

提出了一种名为 SingGAN 的生成对抗网络，用于高保真度歌声合成，该网络通过提出源激发、全局和局部判别器、辅助频谱损失和子带特征匹配惩罚损失等方法，解决了通过文本合成的神经声码器不能直接应用于合成歌声的问题，该网络不仅具有接近现实的音质效果，而且在单个 NVIDIA 2080Ti GPU 上还能实现 50 倍于实时的速度。