  This paper proposes a source-filter-based generative adversarial neural
vocoder named SF-GAN, which achieves high-fidelity waveform generation from
input acoustic features by introducing F0-based source excitation signals to a
neural filter framework. The SF-GAN vocoder is composed of a source module and
a resolution-wise conditional filter module and is trained based on generative
adversarial strategies. The source module produces an excitation signal from
the F0 information, then the resolution-wise convolutional filter module
combines the excitation signal with processed acoustic features at various
temporal resolutions and finally reconstructs the raw waveform. The
experimental results show that our proposed SF-GAN vocoder outperforms the
state-of-the-art HiFi-GAN and Fre-GAN in both analysis-synthesis (AS) and
text-to-speech (TTS) tasks, and the synthesized speech quality of SF-GAN is
comparable to the ground-truth audio.
