PhDHow 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