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Brian Foo’s “Two Trains,” where the song varies depending on median household income for each neighborhood the 2 Train stops in
Created in 2016, Data-Driven DJ is a project by Brian Foo to create musical works through computation and interpretation of real-world data in new and experimental ways. The project is a collection of ten tracks, each sampling data from a broad swath of topics from social to cultural to environmental. Using a blend of programs such as ChucK (a programming language for real-time sound synthesis and music creation), Audacity, Python, Hydrogen (drum machine application), and Processing, each track that Foo creates is compelling and unique, and makes powerful statements when data is not only visualized in his videos, but synthesized into song. What I really like about Foo’s work is that while they may not be catchy, fleshed out songs, each track is fascinating and sends a powerful communication in regards to its subject. Even though the songs are essentially controlled by algorithm, Foo’s artistic touch is still incredibly evident, and the entire body of work shines for that reason.
Brian Foo’s “Rhapsody in Grey,” which uses brain wave data during a seizure to inform the song’s composition
Foo made his process for creating his tracks available as open-source on Github, and you can find more of him on Twitter, Facebook, Soundcloud, and Vimeo.