A Hybrid Approach to Optical Music Recognition With Object Detection and Multimodal LLMs
DOI:
https://doi.org/10.5753/reic.2026.6891Keywords:
Optical music recognition, Deep learning, Object detection, Multimodal LLMs, YOLO, Music transcriptionAbstract
This research introduces a hybrid methodology for Optical Music Recognition (OMR), integrating multimodal language models (LLMs) with contemporary object detection approaches. For clef identification, Gemini 2.0 Flash was employed, capitalizing on its visual and contextual interpretation capabilities, while YOLOv8 and YOLOv11 were adopted for processing pitch value and rhythm detection. This task distribution minimizes object detection complexity, enabling YOLO models to concentrate on precise localization and classification of musical symbols. The proposed methodology demonstrated promising outcomes in the task of recognizing digital monophonic scores, with YOLOv11 achieving a mAP50 of 0.995 in the pitch detection network when clef detection is performed through LLMs.
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Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
