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dc.contributor.authorBeyls, Peter F. E.
dc.contributor.otherFaculty of Arts
dc.contributor.otherFaculty of Science and Engineeringen_US
dc.date.accessioned2011-10-11T11:14:49Z
dc.date.accessioned2011-09-28T11:27:37Z
dc.date.available2011-10-11T11:14:49Z
dc.date.available2011-09-28T11:27:37Z
dc.date.issued2009
dc.date.issued2009
dc.identifierNot availableen_US
dc.identifier.urihttp://hdl.handle.net/10026.1/872
dc.descriptionAccess to the full-text thesis is no longer available at the author's request, due to 3rd party copyright restrictions. Access removed on 28.11.2016 by CS (TIS).
dc.descriptionMetadata merged with duplicate record (http://hdl.handle.net/10026.1/770) on 20.12.2016 by CS (TIS).
dc.descriptionThis is a digitised version of a thesis that was deposited in the University Library. If you are the author please contact PEARL Admin (pearladmin@plymouth.ac.uk) to discuss options.
dc.description.abstract

This thesis suggests a new model of human-machine interaction in the domain of non-idiomatic musical improvisation. Musical results are viewed as emergent phenomena issuing from complex internal systems behaviour in relation to input from a single human performer. We investigate the prospect of rewarding interaction whereby a system modifies itself in coherent though non-trivial ways as a result of exposure to a human interactor. In addition, we explore whether such interactions can be sustained over extended time spans. These objectives translate into four criteria for evaluation; maximisation of human influence, blending of human and machine influence in the creation of machine responses, the maintenance of independent machine motivations in order to support machine autonomy and finally, a combination of global emergent behaviour and variable behaviour in the long run. Our implementation is heavily inspired by ideas and engineering approaches from the discipline of Artificial Life. However, we also address a collection of representative existing systems from the field of interactive composing, some of which are implemented using techniques of conventional Artificial Intelligence. All systems serve as a contextual background and comparative framework helping the assessment of the work reported here. This thesis advocates a networked model incorporating functionality for listening, playing and the synthesis of machine motivations. The latter incorporate dynamic relationships instructing the machine to either integrate with a musical context suggested by the human performer or, in contrast, perform as an individual musical character irrespective of context. Techniques of evolutionary computing are used to optimise system components over time. Evolution proceeds based on an implicit fitness measure; the melodic distance between consecutive musical statements made by human and machine in relation to the currently prevailing machine motivation. A substantial number of systematic experiments reveal complex emergent behaviour inside and between the various systems modules. Music scores document how global systems behaviour is rendered into actual musical output. The concluding chapter offers evidence of how the research criteria were accomplished and proposes recommendations for future research.

en_US
dc.language.isoenen_US
dc.publisherUniversity of Plymouthen_US
dc.titleMusic as complex emergent behaviour : an approach to interactive music systemsen_US
dc.typeThesis
dc.identifier.doihttp://dx.doi.org/10.24382/4218


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