Data-Driven DJ

by Brian Foo

Data-Driven DJ is a series of music experiments that combine data, algorithms, and borrowed sounds.

My goal is to explore new experiences around data consumption beyond the written and visual forms by taking advantage of music's temporal nature and capacity to alter one's mood. Topics will range from social and cultural to health and environmental.

Each song will be made out in the open: the creative process will be documented and published online, and all custom software written will be open-source. Stealing, extending, and remixing are inevitable, welcome, and encouraged. Check out the FAQs for more information.

Track 1: Two Trains Sonification of Income Inequality on the NYC Subway Track 2: Rhapsody in Grey Using Brain Wave Data to Convert a Seizure to Song Track 3: Air Play Smog Music Created With Beijing Air Quality Data Track 4: Mixed Attraction A Song Mixed Using Data On Race And Attraction Track 5: Lee and Jackson The Rhythm and Sounds of Two Painters and Their Work Track 6: Distance From Home Translating Global Refugee Movement to Song Track 7: Too Blue Mapping Coastal Louisiana's Land Loss with Music Track 8: Body Language How Pop Musicians Sing About The Body Track 9: Color Balance A Soundtrack For Gender, Race, and Ethnicity in Movies Track 10: Music Eclipticalis A Song Using the Position of the Stars As Seen From Earth

About me

My name is Brian Foo and I am a programmer and visual artist living and working in New York City. Learn more about what I do on my personal website. You can also follow my work on Twitter, Facebook, Soundcloud, or Vimeo.

Recent Press

For media inquiries, feel free to contact me at hello@brianfoo.com