Improving Optical Music Recognition by Means of Abductive Constraint Logic Programming

In this paper we propose a hybrid system that bridges the gap between traditional image processing methods, used for low-level object recognition, and abductive constraint logic programming used for high-level musical interpretation. Optical Music Recognition (OMR) is the automatic recognition of a scanned page of printed music. All such systems are evaluated by their rate of successful recognition; therefore a reliable OMR program should be able to detect and eventually correct its own recognition errors. Since we are interested in dealing with polyphonic music, some additional complexity is introduced as several concurrent voices and simultaneous musical events may occur. In RIEM, the OMR system we are developing, when events are inaccurately recognized they will generate inconsistencies in the process of voice separation. Furthermore if some events are missing a consistent voice separation may not even be possible. In this work we propose an improved architecture for RIEM to allow the system to hypothesize on possible missing events, to overcome the major failure in the voice assignment due to minor recognition failures. We formalize the process of voice assignment and present a practical implementation using Abductive Constraint Logic Programming. Once we abduce these missing events and know where to look for them in the original score image, we may provide the proper feedback to the recognition algorithms, relaxing the recognition thresholds gradually, until some minimum quality recognition criteria is reached.