  In this paper, we propose an end-to-end Korean singing voice synthesis system
from lyrics and a symbolic melody using the following three novel approaches:
1) phonetic enhancement masking, 2) local conditioning of text and pitch to the
super-resolution network, and 3) conditional adversarial training. The proposed
system consists of two main modules; a mel-synthesis network that generates a
mel-spectrogram from the given input information, and a super-resolution
network that upsamples the generated mel-spectrogram into a linear-spectrogram.
In the mel-synthesis network, phonetic enhancement masking is applied to
generate implicit formant masks solely from the input text, which enables a
more accurate phonetic control of singing voice. In addition, we show that two
other proposed methods -- local conditioning of text and pitch, and conditional
adversarial training -- are crucial for a realistic generation of the human
singing voice in the super-resolution process. Finally, both quantitative and
qualitative evaluations are conducted, confirming the validity of all proposed
methods.
