| dc.description.abstract | Given a piece of music, the timing of each beat varies from performer to performer.
The study of these small differences is known as expressive timing
analysis. Research into expressive timing helps us to understand human perception
of music and the production of enjoyable music. Classical piano music
is one music style where it is possible to measure expressive timing and hence
provides a promising candidate for expressive timing analysis.
Various techniques have been used for expressive timing analysis, such as the
Self-Organising Map (SOM), parabolic regression and Bayesian models. However,
there has been little investigation into whether these methods are in fact
suitable for expressive timing analysis and how the parameters in these methods
should be selected. For example, there is a lack of formal demonstration
that whether the expressive timing within a phrase can be clustered and how
many clusters are there for expressive timing in performed music. In this thesis,
we use a model selection approach to demonstrate that clustering analysis,
hierarchical structure analysis and temporal analysis are suitable for expressive
timing analysis.
Firstly in this thesis, we will introduce some common methods for model
selection such as Akaike's Information Criterion, Bayesian Information Criterion
and cross-validation. Next we use these methods to demonstrate the best model
for clustering expressive timing in piano performances. We propose a number
of pre-processing methods and Gaussian Mixture Models with different settings
for covariance matrices. The candidate models are compared with three pieces
of music, including Balakirev's Islamey and two Chopin Mazurkas. The results
of our model comparison recommend particular models for clustering expressive
timing from the candidate models.
Hierarchical analysis, or multi-layer analysis, is a popular concept in expressive
timing analysis. To compare different hierarchical structures for expressive
timing analysis, we propose a new model that suggests music structure
boundaries according to expressive timing information and hierarchical structure
analysis. We propose a set of hierarchical structures and we compare the
resulting models by showing the probability of observing the boundaries of music
structure and showing the similarity of the same-performer renderings. Our
analysis supports the proposition that hierarchical structure improves the performance
of modelling over non-hierarchical models for the performances that
we considered.
Researchers have also suggested that expressive timing is in
influenced by music
structure and temporal features. In order to investigate this, we consider
four Bayesian graphical models that model dependencies between a position in a
music score and the expressive timing changes in the previous phrase, on expressive
timing in the current phrase. Using our model selection criterion, we find
that the position of a phrase in music scores is only shown to effect expressive
timing in the current phrase when the previous phrase is also considered.
The results in this thesis indicate that model selection is useful in the analysis
of expressive timing. The model selection methods we use here could potentially
be applied to a wide range of applications, such as predicting human perception
of expressive timing in music, providing expressive timing information for music
synthesis and performance identification. | |
