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Nowadays, commercial music has extreme loudness and heavily compresseddynamic range compared to the past. Yet, in music source separation, thesecharacteristics have not been thoroughly considered, resulting in the domainmismatch between the laboratory and the real world. In this paper, we confirmedthat this domain mismatch negatively affect the performance of the music sourceseparation networks. To this end, we first created the out-of-domain evaluationdatasets, musdb-L and XL, by mimicking the music mastering process. Then, wequantitatively verify that the performance of the state-of-the-art algorithmssignificantly deteriorated in our datasets. Lastly, we proposed LimitAug dataaugmentation method to reduce the domain mismatch, which utilizes an onlinelimiter during the training data sampling process. We confirmed that it notonly alleviates the performance degradation on our out-of-domain datasets, butalso results in higher performance on in-domain data.

Towards robust music source separation on loud commercial music