When you listen to a Beethoven piano concerto, pick a song or channel to listen to on the web, send your kids to music lessons, or donate to your local symphony orchestra, do you think of data science? Unlikely. But data science is increasingly critical in the creative process and commercial activities of music. Using Big Data analytics, online music platforms tailor individualized song recommendations for their users; music theorists search for defining features in different types of music; computer scientists develop algorithms that can compose music in any style; behavioral scientists determine how to best engage concert goers; the list is endless. MIDAS recently established the Data Science for Music Research Hub and currently funds four projects.  U-M has incredible depth in data science expertise and a world-class School of Music, Theatre and Dance, and with this hub, MIDAS hopes to:

  • Build a collaborative network of Data Science for Music researchers, and help U-M stay at the national forefront in this research discipline.
  • Promote innovative research ideas, disseminate tools and methods and maximize research sustainability.
  • Form academic and industry partnerships and bring research findings to data science and music education and to the market.

Understanding and Mining Patterns of Audience Engagement and Creative Collaboration in Largescale Crowdsourced Music Performances

This research team will use data mining techniques over the time-evolving networks of audience collaboration to identify audience preferences and interaction patterns.

Understanding How the Brain Processes Music through the Bach Trio Sonatas

This research team will use data mining techniques over the time-evolving networks of audience collaboration to identify audience preferences and interaction patterns.  Such knowledge will be used to improve audience engagement in an audience participatory music performance.

The Sound of Text

Music and words come together in the millions of songs that delight us, and yet for most of the words in the world, their music is silent. This research team will develop algorithms that leverage existing alignments between words and music to produce a musical interpretation for any text.

A Computational Study of Patterned Melodic Structures across Musical Cultures

There is a gap between theoretical and empirical studies of musical structure. This research team will help fill this gap by bringing a renewed theoretical attention to empirical studies of musical structure, focusing on the melody along with pitch, voice, and voice leading.