  Recent progress in singing voice separation has primarily focused on
supervised deep learning methods. However, the scarcity of ground-truth data
with clean musical sources has been a problem for long. Given a limited set of
labeled data, we present a method to leverage a large volume of unlabeled data
to improve the model's performance. Following the noisy self-training
framework, we first train a teacher network on the small labeled dataset and
infer pseudo-labels from the large corpus of unlabeled mixtures. Then, a larger
student network is trained on combined ground-truth and self-labeled datasets.
Empirical results show that the proposed self-training scheme, along with data
augmentation methods, effectively leverage the large unlabeled corpus and
obtain superior performance compared to supervised methods.
