Studying a creative act with computers: Music performance studies with automated discovery methods

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dc.contributor.author Widmer, Gerhard
dc.date.accessioned 2009-01-12T10:45:57Z
dc.date.available 2009-01-12T10:45:57Z
dc.date.issued 2005
dc.identifier.citation Wiggins, G. (2007). Models of musical similarity. Musicae Scientiae, Discussion Forum 4A,315-338.
dc.identifier.uri http://hdl.handle.net/123456789/19349
dc.description.abstract   The purpose of this article is to demonstrate how advanced computer methods may be able to provide new insights into a complex creative activity such as music performance. The context is an inter-disciplinary research project in which Artificial Intelligence (AI) methods are used to analyse patterns in performances by human artists. In asking how the computer can take us closer to an understanding of creativity in music performance, we identify two pertinent research strategies within our project: the use of machine learning algorithms that try to discover common performance principles and thus help separate the rationally explainable aspects of performance from the more genuinely creative ones, and the use of data mining methods that can discover, visualise and describe performance patterns that seem to be characteristic of the style of particular artists and thus may be more directly related to their individual creativity. Some preliminary results are briefly presented that are indicative of the kinds of discoveries these algorithms can make. Some general issues regarding (musical) creativity and its relation to Artificial Intelligence are also briefly discussed.  en
dc.language.iso eng en
dc.title Studying a creative act with computers: Music performance studies with automated discovery methods en
dc.type Article en

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