AI for Music and the Humanities: Insights and Impacts

Join the ML+X community on Tuesday, November 5th, 12-1pm CT in the Orchard View room of the Discovery Building to explore the role of AI/ML in the humanities and music industry—whether by building technical solutions for cultural preservation or tackling ethical questions at the intersection of AI and industry. Register by 11/3 (lunch provided) to guarantee your lunch ticket and join the discussion!

1. What Tune Is That? A Humanities Application of Deep Learning — Alan Ng 
   In this talk, Alan Ng explains the deep learning solution he applied to Irish traditional dance music, using machine learning to identify 'tunes'—unique melodies in that centuries-old aural tradition. By adapting an industry-grade Cover Song Identification model to work on a vast, hand-curated dataset, Alan has created a tool that can instantly recognize traditional Irish tunes, addressing a long-standing challenge in folk music scholarship. He’ll share his perspective on how a hobbyist level of technical literacy was sufficient to let an amateur musicologist build such an advanced technical solution, and ideas for expanding these techniques to other musical traditions and cultural archives.
    
2. Fake Artists, Fake Listeners: AI and the Music Industries — Jeremy Morris
   Several recent controversies have erupted at the intersection of artificial intelligence and the music industries that raised fears over issues of intellectual property, ownership and creativity. While the popular press focused on age-old worries about fraud, deception, and the differences between “real” human practices and “fake” machinic ones, these incidents speak to larger issues about the future role of AI in the everyday work of music industry workers and in the circulation and consumption of popular music. In this talk, media studies researcher Jeremy Morris looks at an overview of these tools and reflects on how the platformization of music (via streaming services like Spotify) has created particular data, institutional, aesthetic, and technical “conditions”; ones where artificially generated artists and listeners are not only possible but an unsurprising outcome.