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dc.contributor.authorCasey, Lauren N.
dc.contributor.authorFine, Benjamin T.
dc.date.accessioned2023-01-09T13:19:39Z
dc.date.available2023-01-09T13:19:39Z
dc.date.issued2022-04
dc.identifier.urihttp://digital.library.wisc.edu/1793/83859
dc.descriptionColor poster with text, images, diagrams, and charts.en_US
dc.description.abstractIn recent years there has been a focus on the accessibility and equity of music instruction in under-served communities. The value of private/guided instruction is clear, but the question of reaching students from lower economic classes presents challenges our current educational system struggles to address. The Blugold Computational Music Suite is a system that supports novice to intermediate musicians in their musical goals by giving them access to computer generated lessons that mimic what a private instructor would assign to them. This work investigates methods for automatically generating musical etudes that are derived from a given musical selection. We compare various methods implemented from the current literature by quantifying the similarities between the generated music and the original selection. Using the similarity results, we make recommendations for which methods should be considered when generating etudes for musical instruction.en_US
dc.description.sponsorshipUniversity of Wisconsin--Eau Claire Office of Research and Sponsored Programsen_US
dc.language.isoen_USen_US
dc.relation.ispartofseriesUSGZE AS589;
dc.subjectInclusive educationen_US
dc.subjectOnline educationen_US
dc.subjectMusic educationen_US
dc.subjectPostersen_US
dc.subjectDepartment of Computer Scienceen_US
dc.titleUnderstanding the Language of Music : From an Etude Generation Perspectiveen_US
dc.typePresentationen_US


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    Posters of collaborative student/faculty research presented at CERCA

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