Source-Free Domain Adaptation for Optical Music Recognition

Adrian Rosello, Eliseo Fuentes-Martínez, María Alfaro-Contreras, David Rizo, Jorge Calvo-Zaragoza

Abstract: This work addresses the problem of Domain Adaptation (DA) in the context of staff-level end-to-end Optical Music Recognition. Specifically, we consider a source-free DA approach to adapt a given trained model to a new collection—an extremely useful scenario for preserving musical heritage. The method involves re-training the pre-trained model to align the statistics stored from the original data in normalization layers with those of the new collection, while also including a regularization mechanism to prevent the model from converging to undesirable solutions. Unlike conventional DA techniques, this approach is very efficient and practical, as it only requires the pre-trained model and unlabeled data from the new collection, without relying on data from the original training collections (i.e., source-free). Evaluation of diverse music collections in Mensural notation and a synthetic-to-real scenario of common Western modern notation demonstrates consistent improvements over the baseline (no DA), often with remarkable relative improvements.