Spectrum sensing is a vital technology for alleviating pressure on the radio spectrum and will become more sophisticated as billions more devices come online. In the future, more advanced techniques utilizing deep learning will sense which parts of the spectrum are available to communicate on. This is a heavily researched area, but few papers demonstrate methods of deploying deep learning on the resource-constrained edge devices that will ultimately use them. One approach called Spectrum Painting augments spectrograms and detects which signals are present with a Convolutional Neural Network. We optimize this method for microcontrollers by simplifying the computation of spectrograms and using TinyML techniques. This results in over 90% accuracy on signals with a high signal-to-noise ratio and a latency of 159 ms on a 64 MHz CPU. Our findings conclude microcontrollers are capable of utilizing deep learning for spectrum sensing, but custom hardware will still be required in a real-world deployment.