Preference-Optimised Music Mastering under Degradation Chains

Music reaches listeners through production, re-recording and distribution stages, and degradations accumulate along the way, several at once and in order. SonicMaster, the prior model for joint mastering and restoration, is trained mostly on single degradations and never on more than three, so it can succeed by correcting each artifact in isolation. We instead train on degradation chains of one to eight degradations (mean 4.8, against 1.6) from 24 types, most of them drawn from six production scenarios, plus pass-through pairs that teach the model when to leave well-mastered music unchanged. On these chains we train Prism, an instruction-conditioned rectified flow from degraded to reference-master latents. Matching a reference alone does not ensure high production quality, so we refine it through a single offline round of preference optimization, ranking the model's own samples by predicted production quality (PQ) and mel-SSIM to the reference. On held-out chains, Prism improves mel-SSIM by $+0.112$ versus $+0.002$ and better preserves already-mastered inputs (0.98 vs. 0.82). Across 574 real low-production-quality releases it raises predicted PQ from $5.36$ to $5.87$, improving 79.3% of tracks, against $+0.09$ for SonicMaster and $+0.32$ for the same architecture trained further on its original corpus. Ablations show that a model trained on the prior corpus barely improves on our chains, and that our advantage holds at every chain depth tested (one to five).