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.

音乐源分离面临标记正确的独立乐器音轨受限的挑战，文章引入了一种自动化技术来改进部分标签错误的数据集，并表明利用改进后的数据集进行训练可以获得与干净标签数据集相当的结果。