分两阶段生成与重建，提升音乐源分离的音质与语义一致性。
DTT-BSR+: A Generative-Regression Cascade for Music Source Restoration
- 先生成符合干净源分布的音轨，再用时域与多分辨率频谱损失优化信号
- 所有音轨的多梅尔信噪比均高于单阶段方法，五类音轨超越顶尖模型X-LANCE
- 揭示了重建精度与语义匹配间的内在权衡，适合音频修复与高质量音乐生成研究者
音乐源恢复（MSR）需同时处理源分离与非线性制作效果的逆向问题。现有方法难以在精确重建目标信号的同时保持语义一致性。为此，我们提出DTT-BSR+，一种两阶段级联式MSR系统，将分布拟合与信号重建分离处理。第一阶段采用生成式DTT-BSR分离器，输出符合干净源先验的音轨；第二阶段通过改进的Demucs网络，结合时域与多分辨率频谱损失增强第一阶段结果。DTT-BSR+在所有音轨上均实现优于单阶段DTT-BSR的多梅尔信噪比（MMSNR），并在五类音轨上超越当前最优的X-LANCE MSR系统。通过弗雷谢音频距离（FAD）分解，我们揭示了音轨间信号重建精度与语义分布拟合之间的隐含权衡。
原文摘要 · Abstract (English)
Music source restoration (MSR) requires jointly addressing source unmixing and the inversion of non-linear production effects. Current methods struggle to achieve accurate target signal reconstruction while maintaining semantic consistency. To address this limitation, we propose DTT-BSR+, a two-stage cascade MSR system that decouples distribution fitting from signal reconstruction into separate stages. A generative DTT-BSR separator in the first stage produces stems matching the prior of clean sources, and a modified Demucs network in the second stage enhances the first stage output using time-domain and multi-resolution spectral losses. DTT-BSR+ improves multi-mel signal-to-noise ratio (MMSNR) over the single-stage DTT-BSR across all stems, and surpasses the state-of-the-art X-LANCE MSR system on five stems. We also reveal through Fréchet Audio Distance (FAD) decomposition an implicit trade-off between signal reconstruction accuracy and semantic distribution fitting across stems.
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