Dance your Ph.D. thesis: Teaching a robot to appreciate beats
Every year, intrepid Ph.D. students face off in a high-stakes competition for honor, glory, and the intermingling of science and art. The goal: Dance your Ph.D. thesis. I showed you the finalists last year. This year, Science magazine has posted all 53 entries online, before the finalists are chosen. I'll confess, I've not yet watched them all. So I can't say this is my favorite, but it is well-done and did immediately catch my attention.
"Human-Based Percussion and Self-Similarity Detection in Electroacoustic Music" is, basically, researcher J. Anderson Mills' attempt to teach a computer to hear percussion sounds the way a human does. In the video, Shiny Robot learns how to dance. You can read a full description of how the various parts of this dance tie into Mills' research at the video site:
The dissertation research began with a two-choice, forced-interval experiment in which 29 humans were asked to rate isolated sounds from most to least percussive. The sound characteristic of rise time was found to be the most correlated with percussion of the characteristics tested. The experiment is represented in the dance by the first two interactions between Alain and Shiny, during which Shiny expresses his inability to correctly choose the stronger percussion sound.
... The final stage of the dissertation research was to use the detection algorithm with real-world music to discover self-similarity in the percussion patterns. By using auto-correlation analysis, the detection algorithm can be used to time the repetition and near repetition in music percussion. Shiny demonstrates the self-similarity of the music by several final repetitve dance moves, repeating appropriately at the time scale of beats, measures, and phrases.
Via Keith Cowing
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Maggie Koerth-Baker
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