‘Texting Scarlatti’: unlocking a standard edition with a digital toolkit

Abstract

Editing musical texts requires a great deal of artistic, intellectual and commercial investment but, once published, an edition tends to become inert, a form of musical still life. ‘Texting Scarlatti’ explores ways of unlocking the value of a standard edition using a variety of digital tools. For as long as the editorial process in music begins and ends with analogue graphical notation, a digital music edition will inevitably be hybrid to some extent. But our project examines each stage in that process from input to output, showing how scholarly insight, performance-based musicology, data science, HCI-inspired crowdsourcing techniques and information systems can together form the basis of an open, rich digital edition for future development. Key outputs will be an extremely large machine-readable dataset consisting of more than 310,000 bars of music, together with structured, searchable apparatus for each of the 555 sonatas in the Scarlatti canon. Musicology has been a notable absentee from recent developments in artificial intelligence and machine learning (AIML) and there is a great deal of catching-up to do. In our conclusion, we urge more scholars and publishers to make similarly large, and largely hidden, datasets more openly available for machine learning and suggest some practical steps to facilitate this.