Handwritten Music Recognition for Mensural Notation: Formulation, Data and Baseline Results
Jorge Calvo-Zaragoza, Alejandro Héctor Toselli, Enrique Vidal · 2017
Music is a key element for cultural transmission, and so large collections of music manuscripts have been preserved over the centuries. In order to develop computational tools for analysis, indexing and retrieval from these sources, it is necessary to transcribe the content to some machine-readable format. In this paper we discuss the Handwritten Music Recognition problem, which refers to the development of automatic transcription systems for musical manuscripts. We focus on mensural notation, one of the most widespread varieties of Western classical music. For that, we present a labeled corpus containing 576 staves, along with a baseline recognition system based on a combination of hidden Markov models and N-gram language models. The baseline error obtained at symbol level is about 40 % which, given the difficulty of the task, can be considered a good starting point for future developments. Our aim is that these data and preliminary results help to promote this research field, serving as a reference in future developments.