  This paper introduces a unified source-filter network with a
harmonic-plus-noise source excitation generation mechanism. In our previous
work, we proposed unified Source-Filter GAN (uSFGAN) for developing a
high-fidelity neural vocoder with flexible voice controllability using a
unified source-filter neural network architecture. However, the capability of
uSFGAN to model the aperiodic source excitation signal is insufficient, and
there is still a gap in sound quality between the natural and generated speech.
To improve the source excitation modeling and generated sound quality, a new
source excitation generation network separately generating periodic and
aperiodic components is proposed. The advanced adversarial training procedure
of HiFiGAN is also adopted to replace that of Parallel WaveGAN used in the
original uSFGAN. Both objective and subjective evaluation results show that the
modified uSFGAN significantly improves the sound quality of the basic uSFGAN
while maintaining the voice controllability.
