U Michigan Researchers Turn to Data Science to Understand Music -- Campus Technology

Four research teams at the University of Michigan will explore the intersection of music and data science thanks to the support of the Michigan Institute for Data Science's (MIDAS) Data Science for Music Challenge Initiative. The challenge asked participants to propose research projects that applied data science tools such as data mining or machine learning to the study of areas such as music theory, the connection between music and words, performance and more. Possible areas of research suggested by the challenge's coordinators include algorithms and computer composition, big data-based instrument deign, music education method analysis, collaborative music making and music recommendation systems, among others. "MIDAS is excited to catalyze innovative, interdisciplinary research at the intersection of data science and music," said Alfred Hero, co-director of MIDAS and the John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science, in a prepared statement. "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."