Document Analysis for Music Scores via Machine Learning

Content within musical documents not only contains musical notation but can also include text, ornaments, annotations, and editorial data. Before any attempt at automatic recognition of elements in these layers, it is necessary to perform a document analysis process to detect and classify each of its constituent parts. The obstacle for this analysis is the high heterogeneity amongst collections, which makes it difficult to propose methods that can be generalizable to a broader range of sources. In this paper we propose a data-driven document analysis framework based on machine learning, which focuses on classifying regions of interest at pixel level. The main advantage of this approach is that it can be exploited regardless of the type of document provided, as long as training data is available. Our preliminary experimentation includes a set of specific tasks that can be performed on music such as the detection of staff lines, isolation of music symbols, and the layering of the document into its elemental parts.