Understanding Optical Music Recognition

For over 50 years, researchers have been trying to teach computers to read music notation, referred to as Optical Music Recognition (OMR). 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. In this work, 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, and (3) proposing a taxonomy of OMR, with most notably a novel taxonomy of applications. Additionally, we discuss how deep learning affects modern OMR research, as opposed to the traditional pipeline. 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

Sistema de reconocimiento de partituras musicales

El objetivo principal del sistema reconocedor de partituras musicales desarrollado en este proyecto, es la elaboración de un programa capaz de analizar partituras, ya sean manuscritas o no manuscritas, y digitalizar sus elementos, pudiendo tratar dichos datos y representarlos en distintos formatos. Para ello, se hará uso de un clasificador simple basado en el algoritmo k-NN (k-Nearest Neighbor) que, junto a una base de datos de símbolos, será capaz de asignar a cada elemento una clase concreta dentro de las mencionadas en dicha base de datos. Por lo tanto, la implementación de este sistema diseña y elabora un sistema tradicional de reconocimiento óptico de música que destaca por la rapidez de ejecución y los resultados obtenidos para ambas clases de partituras (manuscritas o no manuscritas), en contraposición a la gran mayoría de los sistemas OMR desarrollados que centran su atención en los símbolos no manuscritos.-----------------------------------------------------------------------------The main goal of the musical scores recognition system developed in this project is to make a program capable of analizing scores, wheter or not handwritten musical scores, and digitalize their symbols, for be able to treat these data and represent them in various formats. To do this, it will use a simple classifier based on k-NN algorithm (k-Nearest Neighbor) which, with a symbols database, it will be able to assign to each element a class of this database. Therefore, the implementation of this system designed and developed a tradicional system of optical music recognition that highlights the speed of execution and the results for both kinds of scores (handwritten musical scores or not handwritten musical scores), in contrast to most developed OMR systems that focus their work in the recognition of not handwritten musical scores.Ingeniería Técnica en Sonido e Image