How do we know that a melody is a melody? In other words, how does the human brain extract melody from a polyphonic musical context? This thesis begins with a theoretical presentation of musical auditory scene analysis (ASA) in the context of predictive coding and rule-based approaches and takes methodological and analytical steps to evaluate selected components of a proposed integrated framework for musical ASA, unified by prediction. Predictive coding has been proposed as a grand unifying model of perception, action and cognition and is based on the idea that brains process error to refine models of the world. Existing models of ASA tackle distinct subsets of ASA and are currently unable to integrate all the acoustic and extensive contextual information needed to parse auditory scenes. This thesis proposes a framework capable of integrating all relevant information contributing to the understanding of musical auditory scenes, including auditory features, musical features, attention, expectation and listening experience, and examines a subset of ASA issues - timbre perception in relation to musical training, modelling temporal expectancies, the relative salience of musical parameters and melody extraction - using probabilistic approaches. Using behavioural methods, attention is shown to influence streaming perception based on timbre more than instrumental experience. Using probabilistic methods, information content (IC) for temporal aspects of music as generated by IDyOM (information dynamics of music; Pearce, 2005), are validated and, along with IC for pitch and harmonic aspects of the music, are subsequently linked to perceived complexity but not to salience. Furthermore, based on the hypotheses that a melody is internally coherent and the most complex voice in a piece of polyphonic music, IDyOM has been extended to extract melody from symbolic representations of chorales by J.S. Bach and a selection of string quartets by W.A. Mozart.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:766239 |
| Date | January 2018 |
| Creators | SauveĢ, Sarah A. |
| Publisher | Queen Mary, University of London |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://qmro.qmul.ac.uk/xmlui/handle/123456789/46805 |
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