Measuring Expressive Music Performances: a Performance Science Model using Symbolic Approximation

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 assuring the statistical significance of results. This dissertation describes a flexible computational model, based on symbolic approximation of timeseries, 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

Multi-strategy Segmentation of Melodies

Melodic segmentation is a fundamental yet unsolved problem in automatic music processing. At present most melody segmentation models rely on a ‘single strategy’ (i.e. they model a single perceptual segmentation cue). However, cognitive studies suggest that multiple cues need to be considered. In this paper we thus propose and evaluate a ‘multi-strategy’ system to automatically segment symbolically encoded melodies. Our system combines the contribution of different single strategy boundary detection models. First, it assesses the perceptual relevance of a given boundary detection model for a given input melody; then it uses the boundaries predicted by relevant detection models to search for the most plausible segmentation of the melody. We use our system to automatically segment a corpus of instrumental and vocal folk melodies. We compare the predictions to human annotated segments, and to state of the art segmentation methods. Our results show that our system outperforms the state-of-the-art in the instrumental set