An important direction in Renaissance music research is the relationship of repetition to structure and compositional practice. This calls for very time-consuming analysis, and is generally unfeasible when applied to large collections of compositions. However, such largescale analyses are necessary to allow us to identify trends across entire repertories. We describe a system based on the use of suffix arrays, which allows us to find instances of melodic repetition in an enormous body of music, within a reasonable amount of processing time. This system identifies all transpositions, inversions, retrogrades, and retrograde inversions of unknown melodic segments. It has been applied to the entire collection of masses by Palestrina, a corpus of over seven hundred mass sections and approximately one million notes.

A variety of digital data sources-including institutional and formal digital libraries, crowd-sourced community resources, and data feeds provided by media organisations such as the BBC-expose information of musicological interest, describing works, composers, performers, and wider historical and cultural contexts. Aggregated access across such datasets is desirable as these sources provide complementary information on shared real-world entities. Where datasets do not share identifiers, an alignment process is required, but this process is fraught with ambiguity and difficult to automate, whereas manual alignment may be time-consuming and error-prone. We address this problem through the application of a Linked Data model and framework to assist domain experts in this process. Candidate alignment suggestions are generated automatically based on textual and on contextual similarity. The latter is determined according to user-configurable weighted graph traversals. Match decisions confirming or disputing the candidate suggestions are obtained in conjunction with user insight and expertise. These decisions are integrated into the knowledge base, enabling further iterative alignment, and simplifying the creation of unified viewing interfaces. Provenance of the musicologist's judgement is captured and published, supporting scholarly discourse and counter-proposals. We present our implementation and evaluation of this framework, conducting a user study with eight musicologists. We further demonstrate the value of our approach through a case study providing aligned access to catalogue metadata and digitised score images from the British Library and other sources, and broadcast data from the BBC Radio 3 Early Music Show.

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Background in historical musicology A repertory of several thousand secular songs survives from the fifteenth century. Much of it is not attributed to any particular author, and frequently, even the approximate place of origin is uncertain. For us, the origin of a piece is a concern, so that we can better chart the development of musical style. Researchers have tried many approaches to attribution, or to style-classification in a broader sense: manuscript studies of all descriptions, studies of structural elements such as cadence degrees, ornamental style, elements of melodic behaviour such as contour, favoured intervals, and prevalence of leaps; dissonance treatment, and others. However, a comprehensive analysis of all these elements in a sufficiently large body of pieces is too time-consuming for one person to do by hand. Background in music information retrieval Information technology has made it possible to analyze large amounts of data in a reduced timespan, as compared to traditional methods. While this capability has been available for some time, the analysis of multiple musical works by computer is still relatively unexplored in music theory. Modern classification techniques require the extraction of features from sets of data, which are then resolved using higher level constructions. Aims To detail an approach and toolset for feature-set-based analysis of musical works of the fifteenth century as applied to the Buxheim Organ Book, to show some initial results, and to suggest further avenues for musicological exploration of the Buxheim Organ Book and related repertoire. Main contribution Several hundred intabulations of secular songs from the Buxheim Organ Book (ca. 1450–1470) have been analysed to produce individual sets of approximately fifty features using the Humdrum toolkit, as well as specially-constructed software tools. Some of these were general statistical features and others were features commonly examined in style studies of the mid-fifteenth-century secular song repertoire. This paper focuses on details of the initial tools developed for this project, some overall properties of the entire Buxheim set, and their relationship to previous music-theoretical work on the subject. Implications While some researchers have developed useful automated tools for musical analysis, these have rarely been combined with detailed musicological study of earlier repertories. Applying multiple automated tests to a single body of music gives musicologists an opportunity to compare the effectiveness and usefulness of such tools for specific tasks. Solutions specific to the analysis of the chosen repertory have been proposed, and the large-scale results will allow us re-evaluate existing musicological ideas about these pieces.