1. Micó, L.; Oncina, J.; Iņesta, J.M.

, vol. procedings of the ECML/PKDD 12th International Workshop on Machine Learning and Music (MML 2019), pp. . Würzburg (Germany) (2019)

Human supervision is necessary for a correct edition and publication of handwritten early music collections. The output of an op- tical music recognition system for that kind of documents may contain a significant number of errors, making it tedious to correct for a human expert. An adequate strategy is needed to optimize the human feedback information during the correction stage to adapt the classifier to the specificities of each manuscript. In this paper, we compare the perfor- mance of a neural system, difficult and slow to be retrained, and a nearest neighbor strategy, based on the neural codes provided by a neural net, trained offline, used as a feature extractor.