Towards robust music source separation on loud commercial music

TL;DR

This paper investigates how the loudness and compression in modern commercial music cause domain mismatch issues in source separation models, and proposes LimitAug data augmentation to improve robustness across domains.

Contribution

The study introduces out-of-domain datasets mimicking modern music mastering and proposes LimitAug, a novel data augmentation method, to enhance model robustness against domain mismatch.

Findings

Performance drops significantly on out-of-domain datasets.

LimitAug improves robustness and in-domain performance.

Proposed method mitigates effects of loudness and compression.

Abstract

Nowadays, commercial music has extreme loudness and heavily compressed dynamic range compared to the past. Yet, in music source separation, these characteristics have not been thoroughly considered, resulting in the domain mismatch between the laboratory and the real world. In this paper, we confirmed that this domain mismatch negatively affect the performance of the music source separation networks. To this end, we first created the out-of-domain evaluation datasets, musdb-L and XL, by mimicking the music mastering process. Then, we quantitatively verify that the performance of the state-of-the-art algorithms significantly deteriorated in our datasets. Lastly, we proposed LimitAug data augmentation method to reduce the domain mismatch, which utilizes an online limiter during the training data sampling process. We confirmed that it not only alleviates the performance degradation on our out-of-domain datasets, but also results in higher performance on in-domain data.