  XiaoiceSing is a singing voice synthesis (SVS) system that aims at generating
48kHz singing voices. However, the mel-spectrogram generated by it is
over-smoothing in middle- and high-frequency areas due to no special design for
modeling the details of these parts. In this paper, we propose XiaoiceSing2,
which can generate the details of middle- and high-frequency parts to better
construct the full-band mel-spectrogram. Specifically, in order to alleviate
this problem, XiaoiceSing2 adopts a generative adversarial network (GAN), which
consists of a FastSpeech-based generator and a multi-band discriminator. We
improve the feed-forward Transformer (FFT) block by adding multiple residual
convolutional blocks in parallel with the self-attention block to balance the
local and global features. The multi-band discriminator contains three
sub-discriminators responsible for low-, middle-, and high-frequency parts of
the mel-spectrogram, respectively. Each sub-discriminator is composed of
several segment discriminators (SD) and detail discriminators (DD) to
distinguish the audio from different aspects. The experiment on our internal
48kHz singing voice dataset shows XiaoiceSing2 significantly improves the
quality of the singing voice over XiaoiceSing.
