Edinburgh Research Archive

Modelling hierarchical musical structures with composite probabilistic networks

dc.contributor.author
Weiland, Michèle
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dc.date.accessioned
2018-03-29T12:20:53Z
dc.date.available
2018-03-29T12:20:53Z
dc.date.issued
2008
dc.description.abstract
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dc.description.abstract
The thesis is organised as follows:
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dc.description.abstract
• 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.
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dc.description.abstract
• 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.
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dc.description.abstract
• 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.
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dc.description.abstract
• 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 representation.
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dc.description.abstract
• Chapter 6 outlines the design of the software used for the HHMM modelling, and gives details of the software implementation and use.
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dc.description.abstract
• 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.
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dc.description.abstract
• 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.
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dc.description.abstract
• 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.
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dc.description.abstract
• 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.
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dc.identifier.uri
http://hdl.handle.net/1842/29418
dc.publisher
The University of Edinburgh
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dc.relation.ispartof
Annexe Thesis Digitisation Project 2018 Block 17
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dc.relation.isreferencedby
Already catalogued
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dc.title
Modelling hierarchical musical structures with composite probabilistic networks
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dc.type
Thesis or Dissertation
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dc.type.qualificationlevel
Doctoral
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dc.type.qualificationname
PhD Doctor of Philosophy
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