Created by Design I/O, World’s Tiniest Violin is a 'speed project' that uses Google's Project Soli - Alpha Dev Kit combined with the Wekinator machine learning tool and openFrameworks to detect small movements that look like someone playing a tiny violin and translate that to control the playback and volume of a violin solo.
The team used the Project Soli openFrameworks example provided with the ofxSoli addon and searched for the signal that seemed to correlate closest with the tiny violin gesture. In this case it was the fine displacement signal, which then they fed the delta of to Wekinator via OSC. Theo (Design I/O) then had to train Wekinator on what types of finger movements corresponded to playing the violin and which ones it should reject. So he recorded a few different finger movements and assigned the value of 1.0 on the slider. The slider to 0.0 and recorded gestures were then set which didn't correspond: like pulling your hand away from the sensor, or just holding it there without moving your fingers. After a few minutes of recording these gestures, the 'training' was initiated and they were then able to send back an animated value ranging from 0.0 to 1.0 representing how much Theo’s hand looked like it was trying to play a tiny violin. The last step was to map that number to the volume of the violin sample that was being played back by the openFrameworks app.
