  Our previous work, the unified source-filter GAN (uSFGAN) vocoder, introduced
a novel architecture based on the source-filter theory into the parallel
waveform generative adversarial network to achieve high voice quality and pitch
controllability. However, the high temporal resolution inputs result in high
computation costs. Although the HiFi-GAN vocoder achieves fast high-fidelity
voice generation thanks to the efficient upsampling-based generator
architecture, the pitch controllability is severely limited. To realize a fast
and pitch-controllable high-fidelity neural vocoder, we introduce the
source-filter theory into HiFi-GAN by hierarchically conditioning the resonance
filtering network on a well-estimated source excitation information. According
to the experimental results, our proposed method outperforms HiFi-GAN and
uSFGAN on a singing voice generation in voice quality and synthesis speed on a
single CPU. Furthermore, unlike the uSFGAN vocoder, the proposed method can be
easily adopted/integrated in real-time applications and end-to-end systems.
