Knowledge Discovery in Optical Music Recognition: Enhancing Information Retrieval with Instance Segmentation
<|reference_start|>Knowledge Discovery in Optical Music Recognition: Enhancing Information Retrieval with Instance Segmentation: Optical Music Recognition (OMR) automates the transcription of musical notation from images into machine-readable formats like MusicXML, MEI, or MIDI, significantly reducing the costs and time of manual transcription. This study explores knowledge discovery in OMR by applying instance segmentation using Mask R-CNN to enhance the detection and delineation of musical symbols in sheet music. Unlike Optical Character Recognition (OCR), OMR must handle the intricate semantics of Common Western Music Notation (CWMN), where symbol meanings depend on shape, position, and context. Our approach leverages instance segmentation to manage the density and overlap of musical symbols, facilitating more precise information retrieval from music scores. Evaluations on the DoReMi and MUSCIMA++ datasets demonstrate substantial improvements, with our method achieving a mean Average Precision (mAP) of up to 59.70\% in dense symbol environments, achieving comparable results to object detection. Furthermore, using traditional computer vision techniques, we add a parallel step for staff detection to infer the pitch for the recognised symbols. This study emphasises the role of pixel-wise segmentation in advancing accurate music symbol recognition, contributing to knowledge discovery in OMR. Our findings indicate that instance segmentation provides more precise representations of musical symbols, particularly in densely populated scores, advancing OMR technology. We make our implementation, pre-processing scripts, trained models, and evaluation results publicly available to support further research and development.<|reference_end|>
@article{shatri2024knowledge,
    title={Knowledge Discovery in Optical Music Recognition: Enhancing Information
  Retrieval with Instance Segmentation},
    author={Elona Shatri and George Fazekas},
    journal={arXiv preprint arXiv:2408.15002},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.15002},
    primaryClass={cs.IR cs.CV cs.SD}
}