Exploring information retrieval, semantic technologies and workflows for music scholarship: the Transforming Musicology project

Transforming Musicology is a three-year project undertaking musicological research exploring state-of-the-art computational methods in the areas of early modern vocal and instrumental music (mostly for lute), Wagner’s use of leitmotifs, and music as represented in the social media. An essential component of the work involves devising a semantic infrastructure which allows research data, results and methods to be published in a form that enables others to incorporate the research into their own discourse. This includes ways of capturing the processes of musicology in the form of ‘workflows’; in principle, these allow the processes to be repeated systematically using improved data, or on newly discovered sources as they emerge. A large part of the effort of Transforming Musicology (as with any digital research) is concerned with data preparation, which in the early music case described here means dealing with the outputs of optical music recognition software, which inevitably contain errors. This report describes in outline the process of correction and some of the web-based software which has been designed to make this as easy as possible for the musicologist

Duplicate detection in facsimile scans of early printed music

There is a growing number of collections of readily-available scanned musical documents, whether generated and managed by libraries, research projects or volunteer efforts. They are typically digital images; for computational musicology we also need the musical data in machine-readable form. Optical Music Recognition (OMR) can be used on printed music, but is prone to error, depending on document condition and the quality of intermediate stages in the digitization process such as archival photographs. In performing OMR on the British Library’s Early Music Online collection (Pugin and Crawford, 2013) of 16th century volumes we must deal with the problem of images which are rescans of the same pages. These images are not precise digital duplicates of each other, and so must be detected through some approximate means. As well as duplicate scans, there are other forms of similarity present in the collection, such as musical relatedness and movable type reuse. We present our work on developing and combining image-based near-duplicate detection, based on SIFT features (Lowe, 1999), with OMR-based musical content near-duplicate detection. We evaluate an order-statistic based method for finding duplicate scans of pages, and additionally identify a number of distinct kinds of approximate similarity from our distance measures: substantial reuse of graphical material; musical quotation; and title page detection