Beat Detection: A Raspberry Pi Hacking of Hallmark’s “Happy Tappers”

March 9, 2014 0 By Pavan Tumati

In graduate school, time series analysis was the topic I liked the most. The classic models (like AR(p), MA(q), ARMA(p,q), etc.) are used usually after time series have been sampled in some fashion. Of course, there’s more than one way to look at a time series and many of those perspectives come from the field of digital signal processing. Unfortunately, a lot of the text books on DSP are dry. One can spend hours reading a DSP book, understand the math, and still not really appreciate the material. I happen to think the appreciation for the topic comes more from trying to solve real-world problems. As problems are encountered, the motivation arises to go back and attack the mathematics in a meaningful sense. One of the more interesting problems, in my opinion, is real-time beat detection in music.  Therefore,  I decided to do some experimentation with beat detection.

After Christmas, I went through pharmacy after-Christmas sales and picked up a bunch of cheap Hallmark ornaments. When I discovered the ornaments had a port to interface to each other, I decided I’d look at the interfacing method and devise my own schemes for controlling them. After figuring out the communications protocol between ornaments, I experimented by building control systems using FPGAs, Arduinos, and the Raspberry Pi.  I ultimately settled on the Raspberry Pi.

With the Raspberry Pi, I was able to perform FFTs fast enough on the music samples to make a crude attempt at detecting beats while playing mp3 files. The project is still something I tinker with from time to time. I find entertainment in looking at various approaches for beat detection in research papers and documents on the internet.  Below is a sample of what I have been able to create so far, which is a hack of Hallmark’s Happy Tappers, set to a clip Capital Cities’ “Safe and Sound”:

As precious free time becomes available, I’d like to improve the system to work with onset detection and explore various filtering problems.

Finally, I’d like to thank the Dishoom Labs crew (MB) for letting me borrow some equipment during the project.

(Edit:  Removed Flickr Video and replaced with YouTube.)