Bridge Adapters: Learning Restoration Bridges for Music Restoration

Pretrained music generators learn powerful priors for clean, perceptually realistic audio, but restoration requires more than generation: the output must remain faithful to a degraded recording. Existing approaches either train degraded-to-clean restoration flows from scratch, or reuse pretrained generators by conditioning their original noise-to-clean trajectory on the degraded input. We propose Bridge Adapters, a parameter-efficient alternative that changes the transport itself. Starting from a frozen foundation music generator, we train small degradation-specific adapters with a degraded-to-clean flow-matching objective, converting the pretrained generative vector field into a restoration bridge while leaving the backbone unchanged. On the SonicMaster benchmark, spanning nineteen music degradations, Bridge Adapters outperform the prompted SonicMaster all-in-one restoration model across paired reconstruction metrics and Fr'echet Audio Distance, while requiring only a few thousands training steps per degradation. Compared with a ControlNet-style baseline over the same pretrained backbone, adapting the transport yields better restoration quality with substantially fewer sampling steps, reaching strong performance in fewer than five steps. We further show that the adapted bridges inherit the strength of the pretrained prior: stronger music backbones improve both reconstruction and perceptual quality. These results suggest that restoration bridges need not necessarily be learned from scratch; for music restoration, many are only a small adapter away from a pretrained generative model.