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** Information dynamics and temporal structure in music **


It has often been observed that one of the more salient effects
of listening to music to create expectations within the listener,
and that part of the art of making music to create a dynamic interplay
of uncertainty, expectation, fulfilment and surprise. It was not until
the publication of Shannon's work on information theory, however, that 
the tools became available to quantify some of these concepts. 

In this talk, we will examine how a small number of
\emph{time-varying} information measures, such as entropies and mutual
informations, computed in the context
of a dynamically evolving probabilistic model, can be used to characterise
the temporal structue of a stimulus sequence, considered as a random process 
from the point of view of a Bayesian observer.

One such measure is a novel predictive information rate, which we conjecture
may provide an explanation for the `inverted-U' relationship often found between
simple measures of randomness (\eg entropy rate) and
judgements of aesthetic value [Berlyne 1971]. We explore these ideas in the context
of Markov chains using both artificially generated sequences and
two pieces of minimalist music by Philip Glass, showing that even an overly simple
model (the Markov chain), when interpreted according to information dynamic
principles, produces a structural analysis which largely agrees with that of an 
human expert listener and improves on those generated by rule-based methods.