Self-refining of Pseudo Labels for Music Source Separation with Noisy
  Labeled Data
Junghyun Koo Yunkee Chae Chang-Bin Jeon Kyogu Lee
Music source separation (MSS) faces challenges due to the limited
availability of correctly-labeled individual instrument tracks. With the push
to acquire larger datasets to improve MSS performance, the inevitability of
encountering mislabeled individual instrument tracks becomes a significant
challenge to address. This paper introduces an automated technique for refining
the labels in a partially mislabeled dataset. Our proposed self-refining
technique, employed with a noisy-labeled dataset, results in only a 1% accuracy
degradation in multi-label instrument recognition compared to a classifier
trained on a clean-labeled dataset. The study demonstrates the importance of
refining noisy-labeled data in MSS model training and shows that utilizing the
refined dataset leads to comparable results derived from a clean-labeled
dataset. Notably, upon only access to a noisy dataset, MSS models trained on a
self-refined dataset even outperform those trained on a dataset refined with a
classifier trained on clean labels.