核心概念
Advancements in OMR models for piano music recognition are crucial, with a focus on practicality and evaluation metrics.
摘要
The content discusses the challenges faced in Optical Music Recognition (OMR) for pianoform music. It highlights the need for linearized formats like Linearized MusicXML to train end-to-end models effectively. The article emphasizes the importance of evaluating OMR systems accurately, introducing the TEDn metric for direct comparisons. Various datasets like GrandStaff and OLiMPiC are used to test model performance, showcasing state-of-the-art results. The discussion includes limitations, future work, and the potential impact on OMR advancements.
Introduction:
Recent progress in Optical Music Recognition (OMR) focuses on Deep Learning methods.
Challenges arise due to the complex nature of pianoform music notation.
Data Extraction:
"They contain 1,438 and 1,493 pianoform systems."
"Our model surpasses existing models for optical pianoform music recognition."
Quotations:
"We define a sequential format called Linearized MusicXML."
"Evaluating OMR by comparing MusicXML representations has been proposed."
統計資料
They contain 1,438 and 1,493 pianoform systems.
Our model surpasses existing models for optical pianoform music recognition.
引述
"We define a sequential format called Linearized MusicXML."
"Evaluating OMR by comparing MusicXML representations has been proposed."