|These details defined Wed Apr 3 09:58:17 2019||edit|
|Author:||O Doherty, Eamon|
|Title:||Measuring Expressive Music Performances: a Performance Science Model using Symbolic Approximation|
|Degree, institution:||PhD, Dublin Institute of Technology|
|Status, year:||submitted, projected 2019|
|Volumes, pp., etc.:||1 (267), 75000 words|
|Supervisor(s):||Dr Maria McHale, Dr Paul McNulty|
|General specialism:||Musicology: Performance Studies|
|Content, key terms:||Concepts:
|pedagogy, performance dissimilarity, MIR, fraud
Music Performance Science (MPS), sometimes termed systematic musicology in Northern Europe, is concerned with designing, testing and applying quantitative measurements to music performances. It has applications in art musics, jazz and other genres. It is least concerned with aesthetic judgements or with ontological considerations of artworks that stand alone from their instantiations in performances. Musicians deliver expressive performances by manipulating multiple, simultaneous variables including, but not limited to: tempo, acceleration and deceleration, dynamics, rates of change of dynamic levels, intonation and articulation. There are significant complexities when handling multivariate music datasets of significant scale. A critical issue in analyzing any types of large datasets is the likelihood of detecting meaningless relationships the more dimensions are included. One possible choice is to create algorithms that address both volume and complexity. Another, and the approach chosen here, is to apply techniques that reduce both the dimensionality and numerosity of the music datasets while preserving the statistical significance of results. This dissertation describes a flexible computational model, based on symbolic approximation of time- series, that can extract time-related characteristics of music performances to generate performance fingerprints (dissimilarities from an ‘average performance’) to be used for comparative purposes. The model is applied to recordings of Arnold Schoenberg’s Phantasy for Violin with Piano Accompaniment, Opus 47 (1949), having initially been validated on Chopin Mazurkas.1 The results are subsequently used to test hypotheses about evolution in performance styles of the Phantasy since its composition. It is hoped that further research will examine other works and types of music in order to improve this model and make it useful to other music researchers.
In addition to its benefits for performance analysis, it is suggested that the model has clear applications at least in music fraud detection, Music Information Retrieval (MIR) and in pedagogical applications for music education.
|Related publications:||O Doherty, Eamon, ‘Music for Church and Court: Influences on Marco Da Gagliano at Florence and Mantua’ (MA Dissertation, Open University, 2008), 6. O Doherty, Eamon, ‘Measuring Performances: Empirical Musicology in a Pantonal World’, Society for Musicology in Ireland, 5th Annual Postgraduate Conference, (DIT Dublin, 2012) O Doherty, Eamon, ‘Symbolic Approximation: An Approach to Comparing Aspects of Musical Performance’ (presented at the Society for Musicology in Ireland: 7th Annual Postgraduate Students’ Conference, Cork Institute/ Cork School of Music, 2014) O Doherty, Eamon, ‘Saxify: Detecting Fraudulent Music Recordings’ (presented at the SMI/ICTM-IE Annual Postgraduate Conference, Maynooth University: ICTM Ireland/SMI, 2018) O Doherty, Eamon, ‘Music Performance Science: Analytics and Music Recordings’, in Proceedings of the 2016 Music Technology Workshop: Establishing a Partnership Between Music Technology, Business Analytics, and Industry in Ireland (presented at the MustWorks16, UCD, Dublin, 2016), in Proceedings at http://www.ucd.ie/t4cms/MusTWork_2016_Proceedings.pdf, 56–62, Accessed: 20 September 2018|