  Singing voice synthesis is a generative task that involves multi-dimensional
control of the singing model, including lyrics, pitch, and duration, and
includes the timbre of the singer and singing skills such as vibrato. In this
paper, we proposed SU-net for singing voice synthesis named SUSing.
Synthesizing singing voice is treated as a translation task between lyrics and
music score and spectrum. The lyrics and music score information is encoded
into a two-dimensional feature representation through the convolution layer.
The two-dimensional feature and its frequency spectrum are mapped to the target
spectrum in an autoregressive manner through a SU-net network. Within the
SU-net the stripe pooling method is used to replace the alternate global
pooling method to learn the vertical frequency relationship in the spectrum and
the changes of frequency in the time domain. The experimental results on the
public dataset Kiritan show that the proposed method can synthesize more
natural singing voices.
