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dc.contributor.advisorMukherjee, Lopamudra
dc.contributor.advisorNguyen, Hien
dc.contributor.advisorOster, Zachary
dc.contributor.advisorWhitcomb, Benjamin
dc.contributor.authorSzelogowski, Daniel James
dc.date.accessioned2022-11-04T14:54:49Z
dc.date.available2022-11-04T14:54:49Z
dc.date.issued2022-04
dc.identifier.urihttp://digital.library.wisc.edu/1793/83743
dc.descriptionThis file was last viewed in Adobe Acrobat Pro.en_US
dc.description.abstractMusical form analysis is a rigorous task that frequently challenges the expertise of human analysts and signal processing algorithms alike. While numerous systems have been proposed to perform the tasks of musical segmentation, genre classification, and single-label segment classification in popular music, none have specifically focused on the analytical process used by classical musicians. Classical music form analysis facilitates a combination of these tasks, including form classification, structural segmentation, and multilabel large- and small-segment classification – tasks that lack feasible algorithms, machine learning models, and extensive research. Form analysis has many applications in the world of music, and a viable analytical system would greatly benefit performing musicians and academic researchers, both in musicology and signal processing. As well, current datasets used for related research tasks lack standardized analytical conventions, including form classification, and suffer from erroneous annotations and extensibility due to the data sources used for the music. In this thesis, we propose a new system to perform the task of automatic musical form analysis using deep learning models, as well as a new standardized dataset.en_US
dc.language.isoen_USen_US
dc.publisherUniversity of Wisconsin - Whitewateren_US
dc.subjectMusical analysisen_US
dc.subjectMusical formen_US
dc.subjectNeural networks (Neurobiology)en_US
dc.subjectBlended learningen_US
dc.titleDeep learning for musical form: recognition and analysisen_US
dc.typeThesisen_US


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