PhDStatistical approaches are capable of underpinning strong models of musical structure,
perception, and cognition. Multiple viewpoint systems are probabilistic models of sequential
prediction that aim to capture the multidimensional aspects of a symbolic
domain with predictions from multiple finite-context models combined in an information
theoretically informed way. Information theory provides an important grounding
for such models. In computational terms, information content is an empirical measure
of compressibility for model evaluation, and entropy a powerful weighting system for
combining predictions from multiple models. In perceptual terms, clear parallels can be
drawn between information content and surprise, and entropy and certainty. In cognitive
terms information theory underpins explanatory models of both musical representation
and expectation.
The thesis makes two broad contributions to the field of statistical modelling of music
cognition: firstly, advancing the general understanding of multiple viewpoint systems,
and, secondly, developing bottom-up, statistical learning methods capable of capturing
higher order structure.
In the first category, novel methods for predicting multiple basic attributes are empirically
tested, significantly outperforming established methods, and refuting the assumption
found in the literature that basic attributes are statistically independent from one another.
Additionally, novel techniques for improving the prediction of derived viewpoints
(viewpoints that abstract information away from whatever musical surface is under consideration)
are introduced and analysed, and their relation with cognitive representations
explored. Finally, the performance and suitability of an established algorithm that automatically
constructs locally optimal multiple viewpoint systems is tested.
In the second category, the current research brings together a number of existing statistical
methods for segmentation and modelling musical surfaces with the aim of representing
higher-order structure. A comprehensive review and empirical evaluation of
these information theoretic segmentation methods is presented. Methods for labelling
higher order segments, akin to layers of abstraction in a representation, are empirically
evaluated and the cognitive implications explored. The architecture and performance of
the models are assessed from cognitive and musicological perspectives.Media and Arts Technology programme, EPSRC Doctoral Training Centre EP/G03723X/1
