  State-of-the-art singing voice separation is based on deep learning making
use of CNN structures with skip connections (like U-net model, Wave-U-Net
model, or MSDENSELSTM). A key to the success of these models is the
availability of a large amount of training data. In the following study, we are
interested in singing voice separation for mono signals and will investigate
into comparing the U-Net and the Wave-U-Net that are structurally similar, but
work on different input representations. First, we report a few results on
variations of the U-Net model. Second, we will discuss the potential of state
of the art speech and music transformation algorithms for augmentation of
existing data sets and demonstrate that the effect of these augmentations
depends on the signal representations used by the model. The results
demonstrate a considerable improvement due to the augmentation for both models.
But pitch transposition is the most effective augmentation strategy for the
U-Net model, while transposition, time stretching, and formant shifting have a
much more balanced effect on the Wave-U-Net model. Finally, we compare the two
models on the same dataset.
