If you’ve put in all the necessary practice to learn bike tricks, you’d probably like an appropriately dramatic soundtrack to accompany your stunts. A team of students working on a capstone project at the University of Washington took this natural desire a step further with the Music Bike, a system that generates adaptive music in response to the bike’s motion.
The Music Bike has a set of sensors controlled by an ESP32-S3 mounted beneath the bike seat. The ESP32 transmits the data it collects over BLE to an Android app, which in turn uses the FMOD Studio adaptive sound engine to generate the music played. An MPU9250 IMU collects most position and motion data, supplemented by a hall effect sensor which tracks wheel speed and direction of rotation.
When the Android app receives sensor data, it performs some processing to detect the bike’s actions, then uses these to control FMOD’s output. The students tried using machine learning to detect bike tricks, but had trouble with latency and accuracy, so they switched to a threshold classifier. They were eventually able to detect jumps, 180-degree spins, forward and reverse motion, and wheelies. FMOD uses this information to modify music pitch, alter instrument layering, and change the track. The students gave an impressive in-class demonstration of the system in the video below (the demonstration begins at 4:30).
Surprisingly enough, this isn’t the first music-producing bike we’ve featured here. We’ve also seen a music-reactive bike lighting system.
Thanks to [Blake Hannaford] for the tip!
This articles is written by : Nermeen Nabil Khear Abdelmalak
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