  Deep learning based singing voice synthesis (SVS) systems have been
demonstrated to flexibly generate singing with better qualities, compared to
conventional statistical parametric based methods. However, neural systems are
generally data-hungry and have difficulty to reach reasonable singing quality
with limited public available training data. In this work, we explore different
data augmentation methods to boost the training of SVS systems, including
several strategies customized to SVS based on pitch augmentation and mix-up
augmentation. To further stabilize the training, we introduce the
cycle-consistent training strategy. Extensive experiments on two public singing
databases demonstrate that our proposed augmentation methods and the
stabilizing training strategy can significantly improve the performance on both
objective and subjective evaluations.
