Optical Music recognition and Deep Learning: An application to 4-part harmony

Optical Music Recognition (OMR) applied to hand-written scores is widely recognized as a hard real-world problem. The number of different symbols that must be recognized in a score, such as the key, time signatures, tempo, dynamics, notes, alterations, duration, etc, as well as the different meanings some symbols may embody depending on the position in the score, such as a quarter that may mean notes C, D, E, …, makes OMR much harder challenge than Optical Character Recognition (OCR), particularly when dealing with handwritten scores for Computational Intelligence (CI) methods. This paper addresses this hard problem using deep learning based approaches, specif-ically Mask R-CNN, in a specific context: music students that write their scores in a ruled paper notebook when learning 4-part harmony. Preliminary results show that high accuracy levels are obtained, both during training+validation and also during tests, and this allows us to foresee new tools for students that could be combined with available CI methods for 4-part harmony learning.