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. The Michigan Institute for Data Science (MIDAS) has recently launched the Data Science for Music Research Initiative. U-M has incredible depth in data science expertise and a world-class School of Music, Theatre and Dance. With this initiative, MIDAS hopes to help U-M scientists lead the nation in research at the intersection of data science and music.
Currently, this initiative funds four projects that form the Data Science for Music Hub:
- Daniel Forger (Mathematics) and James Kibbie (Organ) lead the project “Understanding how the brain processes music through the Bach trio sonatas”, and will create a library of digitized performances of the Bach Trio Sonatas and analyze common features and errors in these performances.
- Danai Koutra and Walter Lasecki (Electrical Engineering and Computer Science) lead the project “Understanding and mining patterns of audience engagement and creative collaboration in largescale crowdsourced music performances”, and will use data mining techniques to increase audience participation during live performances.
- Rada Mihalcea (Electrical Engineering and Computer Science) and Anıl Çamcı (Performing Arts Technology) lead the project “The sound of text”, and will use neural network architectures to learn sequence-to-sequence mappings and to develop computerized, text-based, music composition.
- Somangshu Mukherji (Music Theory) leads the project “A computational study of patterned melodic structures across musical cultures”, and will examine melodic structures in six different musical corpora using data-science methodologies and put U-M on the map as a pioneer in the emerging research discipline of empirical music theory.
For more on each project, click on the projects below, and see the press release announcing the funding.
Starting from this initial research, the MIDAS Data Science for Music Hub 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.
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