# Towards robust music source separation on loud commercial music

arXiv: https://arxiv.org/abs/2208.14355 · HTML: https://mlsanity.com/paper/2208.14355/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2022.

## Built on by (most cited first, 2 of 2)

- [Moisesdb: A dataset for source separation beyond 4-stems](https://mlsanity.com/paper/2307.15913/) (2023)
- [Self-refining of Pseudo Labels for Music Source Separation with Noisy Labeled Data](https://mlsanity.com/paper/2307.12576/) (2023)

## Abstract (arXiv)

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.