After learning about the basics of embedded ML, industrial designer and educator Phil Caridi had the idea to build a metal detector, but rather than using a coil of wire to sense eddy currents, his device would use a microphone to determine if metal music is playing nearby.
Caridi started out by collecting around two hours of music and then dividing the samples into two labels: “metal” and “non_metal” using Edge Impulse. After that, he began the process of training a neural network after passing each sample through an MFE filter.
The end result was a model capable of detecting if a given piece of music is either metal or non-metal with around 88.2% accuracy. This model was then deployed onto a Nano 33 BLE Sense, which tells the program what kind of music is playing, but Caridi wasn’t done yet. He also 3D-printed a mount and gauge that turns a needle further to the right via a servo motor as the confidence of “metal music” increases.
As seen in his video, the device successfully shows the difference between the band Death’s “Story to Tell” track and the much tamer and non-metal song “Oops!… I Did It Again” by Britney Spears. For more details about this project, you can read Caridi’s blog post.