The first and most useful is a set of 90 tracks choreographed by a prolific author who goes by the pseudonym Fraxtil. The system was trained on two sets of real-world data. Middle: The neural network's predicted next steps. Top: A real stepchart for Anamanaguchi's "Mess," choreographed by Fraxtil. Instead, it samples every 10 milliseconds or so to determine what moments should correspond to steps. The step placement part is especially interesting the system skips the intermediate step of recognizing tempo or beat structure. Second, step selection determines which steps (left, right, up, down, etc.) to place. First, it determines step placement - points in time at which there should be a step.
I was playing with that for a while, and eventually it occurred to me that a much more interesting and novel problem might be to just try to recreate the game, rather than simply use the data as just sort of a convenient source.”Īnd that’s essentially what Dance Dance Convolution does. And I just had this idea to use that for tempo detection. “It sort of dawned on me one day that I had somewhere buried deep in my hard drive, gigabytes and gigabytes of data from this game StepMania, from a folder I’d been transferring from computer to computer since I was a teenager. But it occurred to him that StepMania packs already contain tons of annotated musical data. It’s notoriously difficult to obtain large samples of music with metadata about beat and tempo. The project began when Chris Donahue, longtime DDR player and machine learning researcher, was trying to solve one of the big problems in the field of music information retrieval (essentially, the extraction of data about music from recordings).
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