Wagner Ring Dataset: A Complex Opera Scenario for Music Processing and Computational Musicology

This paper introduces the Wagner Ring Dataset (WRD), a multi-modal and multi-version resource on the large-scale opera cycle Der Ring des Nibelungen by Richard Wagner. The Ring comprises four music dramas organized into eleven acts and 21 939 measures in total. Concerning sheet music, we processed a publicly available piano reduction (822 pages) of the full score with optical music recognition followed by extensive manual corrections to create a high-quality, machine-readable symbolic score. Concerning audio data, our corpus covers 16 recorded performances of the full Ring (three of which are publicly available thanks to copyright expiry), each lasting about 14–15 hours. To musically synchronize these versions among each other, we manually annotated all measure positions for three performances, which we transferred to the remaining performances via automated synchronization techniques. The dataset further comprises annotations of key and time signatures, scenes, and singing voice regions (libretto). Moreover, we provide note event annotations for all performances derived from the piano score. The WRD thus constitutes a comprehensive resource for developing algorithms for various music information retrieval tasks, complementing existing datasets with a complex opera scenario. For computational musicology, the WRD serves as a structured dataset that allows for studying the composition and performances of the Ring

Computer Aided Statistical Analysis of Motive Use and Compositional Idiom

This thesis discusses the creation of a means of pitch-based data representation which allows automated logging and analysis of melodic motivic material. This system also allows analysis of a number of attributes of a composition which are not readily apparent to human analysis. By using a numerical data format which treats motivically related material as equivalent, groups of tonally equivalent intervals (n-tuples) can be logged and have statistical procedures carried out on them. This thesis looks at four applications of this approach: measuring the most commonly occurring motivic material; creating a transition matrix showing probabilities of movement between intervals; measuring the extent of disjunct or conjunct writing; and measuring concentration of motivic writing (the extent to which motives are reused). Following the discussion of the data representation system, a set of expositions taken from the piano sonatas of Haydn, Mozart, and Clementi are converted to this method of data representation, and results are collected for the above four applications. The implications of the results of this analysis are discussed, and further potential applications of the system are explored

An empirical investigation of the concept of memes in music using mass data analysis of string quartets

Dawkins introduced the concept of the meme as the cultural equivalent to the gene (1989, pp. 189-201). To illustrate the concept, Dawkins cited ‘tunes, ideas, catch-phrases, clothes, fashions, ways of making pots or of building arches’ (1989, p. 192) as examples of memes. All of Dawkins’ examples are elements of culture that have evolved over time. Because music is a part of culture, then under Dawkins’ hypothesis, memes should exist in music. After all, the first of Dawkins’ examples was a ‘tune’. Jan expanded on Dawkins’ ideas with a thorough investigation into memes in music (2007). This was done on a number of different levels within music, from melodic lines to overall structure, using a range of examples within music. Whilst providing a strong case for memes, Jan was not able to provide evidence from an analysis encompassing a large dataset of music. However, Jan does provide a number of possible methodologies for analysing memes in music, including investigating memes across time periods using single lines of notes (2007, p. 211). The present research expands on Jan’s suggested methodology by looking at short monophonic three- to eleven-note patterns in music across five different non-traditional musicological time periods within a large dataset of string quartets. A search for memes in music is conducted using a range of scores. These are converted to MusicXML documents, which are then imported into a relational database. Data mining is then implemented on the resultant dataset to produce a series of ranking positions for monophonic note patterns within the music based upon the relative frequencies of their appearances within specified time periods. Additionally, a similarity algorithm is used to investigate the possible ancestral relationships between different monophonic note patterns. Within the limitations of the working definitions and assumptions made in the research, it was shown that there is evidence for the evolutionary properties of selection, replication and variation, and the replicator properties of longevity, fecundity and copying fidelity for some monophonic note patterns within the dataset