Four U-M research teams will receive support for projects that apply data science tools to the study of music theory, performance, social media-based music making, and the connection between words and music. The funding is provided under the Data Science for Music Challenge Initiative through the Michigan Institute for Data Science (MIDAS). The projects include digital analysis of Bach sonatas to mining data from crowd-sourced compositions.
“The four proposals selected will apply and demonstrate some of the most powerful state-of-the-art machine learning and data mining methods to empirical music theory, automated musical accompaniment of text, and data-driven analysis of music performance,” said Alfred Hero, co-director of MIDAS and professor of electrical engineering and computer science. Jason Corey, associate dean for graduate studies and research at the School of Music, Theatre & Dance, added: “These new collaborations between our music faculty and engineers, mathematicians and computer scientists will help broaden and deepen our understanding of the complexities of music composition and performance.”