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dc.contributor.authorWeeden, Rohan E.
dc.date.accessioned2020-04-02T22:29:30Z
dc.date.available2020-04-02T22:29:30Z
dc.date.issued2019-05
dc.identifier.urihttp://hdl.handle.net/11122/10957
dc.descriptionMaster's Project (M.S.) University of Alaska Fairbanks, 2019en_US
dc.description.abstractIn this paper, we compare the effectiveness of two different types of Recurrent Neural Networks, fully connected and Long Short Term Memory, for modeling music compositions. We compare both the categorical accuracies of these models as well as the quality of generated compositions, and find that the model based on Long Short Term Memory is more effective in both cases. We find that the fully connected model is not capable of generating non repeating note sequences longer than a few measures, and that the Long Short Term Memory model can do significantly better in some cases.en_US
dc.language.isoen_USen_US
dc.titleToward computer generated folk music using recurrent neural networksen_US
dc.typeThesisen_US
dc.type.degreemsen_US
dc.identifier.departmentDepartment of Computer Scienceen_US
dc.contributor.chairLawlor, Orion
dc.contributor.committeeChappell, Glenn
dc.contributor.committeeGenetti, Jon
refterms.dateFOA2020-04-02T22:29:31Z


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