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dc.contributor.authorWitten, Ian H.en_NZ
dc.contributor.authorManzara, Leonard C.en_NZ
dc.contributor.authorConklin, Derrellen_NZ
dc.date.accessioned2016-02-18T01:30:46Z
dc.date.available1992en_NZ
dc.date.available2016-02-18T01:30:46Z
dc.date.issued1992en_NZ
dc.identifier.citationWitten, I. H., Manzara, L. C., & Conklin, D. (1992). Comparing human and computational models of music prediction (Computer Science Working Papers 92/4). Hamilton, New Zealand: Department of Computer Science, University of Waikato.en
dc.identifier.issn1170-487Xen_NZ
dc.identifier.urihttps://hdl.handle.net/10289/9923
dc.description.abstractThe information content of each successive note in a piece of music is not an intrinsic musical property but depends on the listener's own model of a genre of music. Human listeners' models can be elicited by having them guess successive notes and assign probabilities to their guesses by gambling. Computational models can be constructed by developing a structural framework for prediction, and "training" the system by having it assimilate a corpus of sample compositions and adjust its internal probability estimates accordingly. These two modeling techniques turn out to yield remarkably similar values for the information content, or "entropy," of the Bach chorale melodies. While previous research has concentrated on the overall information content of whole pieces of music, the present study evaluates and compares the two kinds of model in fine detail. Their predictions for two particular chorale melodies are analyzed on a note-by-note basis, and the smoothed information profiles of the chorales are examined and compared. Apart from the intrinsic interest of comparing human with computational models of music, several conclusions are drawn for the improvement of computational models.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherDepartment of Computer Science, University of Waikatoen_NZ
dc.relation.ispartofseriesComputer Science Working Papers
dc.rights© 1992 Ian H. Witten, Leonard C. Manzara & Darrell Conklin
dc.subjectMachine learning
dc.titleComparing human and computational models of music predictionen_NZ
dc.typeWorking Paper
uow.relation.series92/4
dc.relation.isPartOfWorking Paper Seriesen_NZ
pubs.confidentialfalseen_NZ
pubs.elements-id137070
pubs.place-of-publicationHamilton, New Zealand


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