Deep learning for musical form: recognition and analysis
Date
2022-04Author
Szelogowski, Daniel James
Publisher
University of Wisconsin - Whitewater
Advisor(s)
Mukherjee, Lopamudra
Nguyen, Hien
Oster, Zachary
Whitcomb, Benjamin
Metadata
Show full item recordAbstract
Musical 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.
Subject
Musical analysis
Musical form
Neural networks (Neurobiology)
Blended learning
Permanent Link
http://digital.library.wisc.edu/1793/83743Description
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