  Supervised deep learning methods for performing audio source separation can
be very effective in domains where there is a large amount of training data.
While some music domains have enough data suitable for training a separation
system, such as rock and pop genres, many musical domains do not, such as
classical music, choral music, and non-Western music traditions. It is well
known that transferring learning from related domains can result in a
performance boost for deep learning systems, but it is not always clear how
best to do pretraining. In this work we investigate the effectiveness of data
augmentation during pretraining, the impact on performance as a result of
pretraining and downstream datasets having similar content domains, and also
explore how much of a model must be retrained on the final target task, once
pretrained.
