Optical Music Recognition (OMR) is the research field that investigates how the computer can learn to read those documents. Throughout the centuries, millions of songs were composed and written down in documents using music notation.
Music is an essential part of our culture and heritage.
Based on this work, the reader should be able to attain a basic understanding of OMR: its objectives, its inherent structure, its relationship to other fields, the state of the art, and the research opportunities it affords. Additionally, we discuss how deep learning affects modern OMR research, as opposed to the traditional pipeline. In this tutorial, we address these shortcomings by (1) providing a robust definition of OMR and its relationship to related fields, (2) analyzing how OMR inverts the music encoding process to recover the musical notation and the musical semantics from documents, (3) proposing a taxonomy of OMR, with most notably a novel taxonomy of applications. However, this field is still difficult to access for new researchers, especially those without a significant musical background: few introductory materials are available, and furthermore the field has struggled with defining itself and building a shared terminology. author = įor over 50 years, researchers have been trying to teach computers to read music notation, referred to as Optical Music Recognition (OMR). In particular, we discuss aspects on music representations, music synchronization, and optical music recognition, while indicating various strategies and open research problems. In this paper, we address the problem of bridging the gap between the sheet music domain and the audio domain. In particular, the nuances and subtleties of musical performances, which are generally not written down in the score, make the music come alive. On the contrary, most people enjoy music by listening to audio recordings, which represent music in an acoustic form. Because of its explicitness and compactness, most musicologists discuss and analyze the meaning of music on the basis of sheet music. Sheet music describes abstract high-level parameters such as notes, keys, measures, or repeats in a visual form.
Sheet music and audio recordings represent and describe music on different semantic levels.