• Chapter 2 provides background information on existing research in the field
of computational music harmonisation and generation, as well as some the¬
oretical background on musical structures. Finally, the chapter concludes
with an outline of the scope and aims of this research.
• Chapter 3 provides a short overview of the field of Machine Learning, ex¬
plaining concepts such as entropy measures and smoothing. The definitions
of Markov chains and Hidden Markov models are introduced together with
their methods of inference.
• Chapter 4 begins with the definition of Hierarchical Hidden Markov models
and techniques for linear time inference. It continues by introducing the new
concept of Input-Output HHMMs, an extension to the hierarchical models
that is derived from Input-Output HMMs.
• Chapter 5 is a short chapter that shows the importance of the music rep¬
resentation and model structures for this research, and gives details of the
• Chapter 6 outlines the design of the software used for the HHMM modelling, and gives details of the software implementation and use.
• Chapter 7 describes how dynamic networks of models were used for the
generation of new pieces of music using a "random walk" approach. Several
different types of networks are presented, exploring the different possibilities
of layering the musical structures and organising the networks.
• Chapter 8 tries to evaluate musical examples that were generated with sev¬
eral different types of networks. The evaluation process is both subjective
and objective, using the results of a listening experiment as well as cross
entropy measures and musical theoretical rules.
• Chapter 9 offers a discussion of the methodology of the approach, the con¬
figuration and design of networks and models as well as the learning and
generation of the new musical structures.
• Chapter 10 concludes the thesis by summarising the research's contribu¬
tions, evaluating whether the project scope has been fulfilled and the major
goals of the research have been met.