通过两阶段方法恢复混音中的原始音频源，效果优于所有基线。
Music Source Restoration with Ensemble Separation and Targeted Reconstruction
- 先用多个预训练分离模型生成初步估计，再用修复模型精细化重建。
- 在官方评测中各项指标均超越基线，排名第二。
- 适合音乐修复、音频还原领域研究者参考。
首届音乐源恢复（MSR）挑战赛旨在从完全混合且经过母带处理的音乐中恢复原始未处理的音轨。与传统的音乐源分离不同，MSR需要逆转均衡、压缩、混响等复杂的制作过程及现实世界中的退化。为此，我们提出一个两阶段系统：首先，使用一组预训练的分离模型生成初步的源信号估计；随后，利用一组基于BSRNN的预训练修复模型对这些估计进行针对性重构以提升质量。在官方的MSR基准测试中，我们的系统在所有指标上均超过基线，位列所有提交方案中的第二名。代码已公开于https://github.com/xinghour/Music-source-restoration-CUPAudioGroup。
原文摘要 · Abstract (English)
The Inaugural Music Source Restoration (MSR) Challenge targets the recovery of original, unprocessed stems from fully mixed and mastered music. Unlike conventional music source separation, MSR requires reversing complex production processes such as equalization, compression, reverberation, and other real-world degradations. To address MSR, we propose a two-stage system. First, an ensemble of pre-trained separation models produces preliminary source estimates. Then a set of pre-trained BSRNN-based restoration models performs targeted reconstruction to refine these estimates. On the official MSR benchmark, our system surpasses the baselines on all metrics, ranking second among all submissions. The code is available at https://github.com/xinghour/Music-source-restoration-CUPAudioGroup
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