This report describes the system submitted to the music source restoration (MSR) Challenge 2025. Our approach is composed of sequential BS-RoFormers, each dealing with a single task including music source separation (MSS), denoise and dereverb. To support 8 instruments given in the task, we utilize pretrained checkpoints from MSS community and finetune the MSS model with several training schemes, including (1) mixing and cleaning of datasets; (2) random mixture of music pieces for data augmentation; (3) scale-up of audio length. Our system achieved the first rank in all three subjective and three objective evaluation metrics, including an MMSNR score of 4.4623 and an FAD score of 0.1988. We have open-sourced all the code and checkpoints at https://github.com/ModistAndrew/xlance-msr.

翻译：本报告描述了提交至音乐源修复（MSR）挑战赛2025的系统。我们的方法由级联的BS-RoFormer模型构成，每个模型分别处理音乐源分离、降噪和去混响三项子任务。为支持任务要求的8种乐器，我们采用了音乐源分离领域预训练的模型检查点，并通过多种训练策略对分离模型进行微调，包括：（1）数据集的混合与清洗；（2）随机混合音乐片段的数据增强；（3）音频时长的尺度扩展。我们的系统在所有三项主观评价指标和三项客观评价指标中均位列第一，其中MMSNR分数达到4.4623，FAD分数为0.1988。所有代码与模型检查点已在https://github.com/ModistAndrew/xlance-msr开源。