In this report, we present our award-winning solutions for the Music Demixing Track of Sound Demixing Challenge 2023. First, we propose TFC-TDF-UNet v3, a time-efficient music source separation model that achieves state-of-the-art results on the MUSDB benchmark. We then give full details regarding our solutions for each Leaderboard, including a loss masking approach for noise-robust training. Code for reproducing model training and final submissions is available at github.com/kuielab/sdx23.

翻译：在本报告中，我们介绍了在声音分离挑战赛2023音乐分离赛道中获奖的解决方案。首先，我们提出了TFC-TDF-UNet v3，这是一种高效的音乐源分离模型，在MUSDB基准测试中取得了最先进的结果。随后，我们详细阐述了针对各排行榜的解决方案，包括一种用于噪声鲁棒训练的损失掩码方法。实现模型训练及最终提交的代码已开源至github.com/kuielab/sdx23。