Daniel Szelogowski graduated with a Masters in Computer Science in Spring 2022 from UW-Whitewater. Prior to that he received his Bachelor’s degree in Music Education in 2021. During his graduate studies, he pursed research in the area of Classical music form analysis and Deep Learning. He was able to see the computational needs in the area of classical music analysis, where few such methods exists. He also recognized that machine learning tools are useless without appropriate data to learn from. To this end, he curated a dataset of 200 musical pieces which were manually labeled for form and phrase. He also developed new machine learning models that could use this data to predict the musical phrase and structure of a new piece. This idea has a number of applications in music education and pedagogy, but can also be extended to make classical musical pieces more searchable in digital databases. His thesis work was published in International Conference of Music Information Retrieval 2022 under the title “ A Novel dataset and deep learning benchmark for classical music form recognition and analysis”. His thesis was selected by a graduate faculty committee as the Outstanding Thesis of 2023 for the University of Wisconsin-Whitewater.
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