End-to-End Historical Music Restoration in Latent Space

Abstract:Historical music restoration (HMR) has almost exclusively focused on constrained problems such as Super-Resolution or the restoration of solo pieces, under-exploring the general task of restoring orchestral historical music, which has multiple instruments. This under-exploration is largely because the HMR domain, early-20th-century recordings, has no pre-degradation ground-truth pairs, making the restoration task unsupervised and more challenging. This paper presents a supervised end-to-end orchestral HMR benchmark by exploring both the synthetic degradation functions and the end-to-end generative deep-learning restoration methods. We simulate the historical recording degradation chain more faithfully than prior work, which makes orchestral restoration into a tractable supervised problem. A latent flow-matching model trained on the resulting synthetic pairs outperforms existing HMR baselines on intrusive, non-intrusive, and subjective evaluations. We also curate and release a 9.3-hour license-free, unpaired, historical classical-music test set, along with code and audio demos.

5 pages, 2 figures, 3 tables; submitted to ICASSP 2027. Code and audio demos available at the project repository

Audio and Speech Processing (eess.AS); Machine Learning (cs.LG); Sound (cs.SD); Signal Processing (eess.SP)

arXiv:2610.00607 [eess.AS]

(or arXiv:2610.00607v1 [eess.AS] for this version)

https://doi.org/10.48550/arXiv.2610.00607 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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