Optical Music Recognition Workflow for Neume Notation and its Encoding & The Art of Teaching Computers

Closing keynote of the Music Encoding Conference 2022

Proposal to Adopt MEI for Encoding Traditional Japanese Music Scores

Encoding of western music notation is being standardized as a result of the dedicated work of the MEI and MusicXML communities. However, there is no machine-readable format for encoding traditional Japanese musical notation. This short paper discusses the use of MEI to encode traditional Japanese musical notation

Mysterium: A Corpus of Alexander Scriabin's Music for Solo Piano

A new digital encoding of 207 works by Alexander Scriabin is reported. The corpus includes all of Scriabin’s works for solo piano with an opus number. Each work is in the **kern format, having first been encoded into Finale, exported to a MusicXML file, and then converted using the musicxml2hum command. The corpus’s content and method of encoding are detailed, as well as some complications of the encoding process

»Play it again, Sam« – Levels of Complexity in Encoding Performance Personnel

Capturing the personnel needed to perform a musical work in MEI metadata is straightforward with standard ensemble configurations, such as string quartets. In contrast, it can be highly complex for extensive orchestral settings, stage music, or, e.g., twentieth-century ‘Neue Musik.’ Especially in the latter case, the degree of possible variation is virtually limitless. While MEI 4.0.1 offers places within (descendants of , ) for capturing such data and provides means for quite complicated data structures through, e.g., nesting or referencing, there is still room for improvement. First of all, data structures should stay as simple and generic as possible. That is to say, that structural modification and a more detailed description of MEI's data model for the benefit of a more concise encoding should be the target, especially when envisioning a more structured encoding of more complex setups. For example, representing dependencies between performers and instruments is extremely limited in the data model for in MEI 4.0.1 (see definition of and also Gubsch & Ried, 2021). This poster takes as a starting point issues from two edition projects dealing with music from the twentieth century to illustrate philological intricacies and investigate the possibilities to encode them with MEI 4.0.1

Sharing MEI: common semantics in diverse musics?

In this panel, we consider the role of MEI in providing common structures and meanings for heterogeneous musical practices and notations and, to a lesser extent, uses. Drawing on direct experience of working with particular cultural or historical material, we consider the robustness of the fundamental modelling of MEI, and its challenges and strengths. From a practical standpoint, we evaluate strategies for successfully working with MEI, whether through extension of the standard or linking to it from external data structures. We will engage the community with the problems of standardising musical semantics from a non-CMN (Common Music Notation) perspective, and will start the process of developing recommendations for those who wish to engage with the standard to extend further our range of digitised and shareable musics

Musicologists and Data Scientists Pull out all the Stops: Defining Renaissance Cadences Systematically

Digital tools offer many ways to find musical patterns with machines. But the task of formulating digital-musical queries systematically, interpreting the results, and refining our methods to yield intelligent insights about musical practice is far more difficult. In this presentation, a team of musicologists and data scientists will share our experiences in developing CRIM Intervals, a Python-Pandas toolkit designed to support Citations: The Renaissance Imitation Mass, modeling human expertise in terms that can be used by computers to analyze encoded musical scores, and presenting the results of automated score-reading in forms that scholars can interrogate and refine. This presentation explains how we developed these tools, from understanding the constraints that define a given musical event, to the development of the tools needed to model those constraints, and in turn to the stages of refinement needed to eliminate false negatives and positives