  In the recent years, singing voice separation systems showed increased
performance due to the use of supervised training. The design of training
datasets is known as a crucial factor in the performance of such systems. We
investigate on how the characteristics of the training dataset impacts the
separation performances of state-of-the-art singing voice separation
algorithms. We show that the separation quality and diversity are two important
and complementary assets of a good training dataset. We also provide insights
on possible transforms to perform data augmentation for this task.
