Music Symbol Detection with Faster R-CNN Using Synthetic Annotations

Kwon-Young Choi, Bertrand Coüasnon, Yann Ricquebourg, Richard Zanibbi

Abstract:

Accurately detecting music symbols in images of historical, complex, dense orchestral or piano printed scores can be challenging due to old printing techniques or time degradations.
Because segmentation problems can vary widely, a data driven approach like the use of of deep learning detectors is needed.
However, the production of detection annotations (symbol bounding boxes + classes) for such systems is costly and time consuming.
We propose to train such model with synthetic data and annotations produced by a music program.
We analyze which classes are relevant to the detection task and present a first selection of music score typesetting files that will be used for training.
To evaluate our model, we plan to compute quantitative results on a synthetic test set and provide qualitative results on a few manually annotated historical music scores.

Keywords: Optical Music Recognition, Symbol Detection, Synthetic Data

TL;DR: Music symbol detection in printed music scores using Deep Learning detectors and synthetic data annotations