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dc.contributor.authorKirlin, Phillip B.en_US
dc.contributor.authorYust, Jasonen_US
dc.coverage.spatialWesleyan, CTen_US
dc.date.accessioned2020-01-22T19:44:06Z
dc.date.available2020-01-22T19:44:06Z
dc.date.issued2017-04-04
dc.identifier.citationPhillip B. Kirlin & Jason Yust (2016) Analysis of analysis: Using machine learning to evaluate the importance of music parameters for Schenkerian analysis, Journal of Mathematics and Music, 10:2, pp. 127-148. https://doi.org/10.1080/17459737.2016.1209588
dc.identifier.urihttps://hdl.handle.net/2144/39133
dc.description.abstractWhile criteria for Schenkerian analysis have been much discussed, such discussions have generally not been informed by data. Kirlin [Kirlin, Phillip B., 2014 “A Probabilistic Model of Hierarchical Music Analysis.” Ph.D. thesis, University of Massachusetts Amherst] has begun to fill this vacuum with a corpus of textbook Schenkerian analyses encoded using data structures suggested byYust [Yust, Jason, 2006 “Formal Models of Prolongation.” Ph.D. thesis, University of Washington] and a machine learning algorithm based on this dataset that can produce analyses with a reasonable degree of accuracy. In this work, we examine what musical features (scale degree, harmony, metrical weight) are most significant in the performance of Kirlin's algorithm.en_US
dc.relation.ispartofJournal of Mathematics and Music
dc.subjectSchenkerian analysisen_US
dc.subjectMachine learningen_US
dc.subjectHarmonyen_US
dc.subjectMelodyen_US
dc.subjectMathematicsen_US
dc.subjectComputer scienceen_US
dc.subjectSound and music computingen_US
dc.subjectRhythmen_US
dc.subjectFeature selectionen_US
dc.titleAnalysis of analysis: importance of different musical parameters for Schenkerian analysisen_US
dc.typeArticleen_US
dc.description.versionAccepted manuscripten_US
dc.identifier.doi10.1080/17459737.2016.1209588
pubs.elements-sourcemanual-entryen_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Fine Artsen_US
pubs.organisational-groupBoston University, College of Fine Arts, School of Musicen_US
dc.identifier.mycv348971


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