Self-refining of Pseudo Labels for Music Source Separation with Noisy Labeled Data

• URL: http://arxiv.org/abs/2307.12576v1
• Date: Mon, 24 Jul 2023 07:47:21 GMT
• Title: Self-refining of Pseudo Labels for Music Source Separation with Noisy Labeled Data
• Authors: Junghyun Koo, Yunkee Chae, Chang-Bin Jeon, Kyogu Lee
• Abstract summary: Music source separation (MSS) faces challenges due to the limited availability of correctly-labeled individual instrument tracks. 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.
• Score: 15.275949700129797
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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