Guitar effects pedals are designed to provide an alteration to a guitar signal through electronic means and are often controlled by a footswitch that routes the signal either through the effect or directly to the output through a 'clean' channel. Because players often switch the effects on and off during different portions of a song and different playing styles, our goal in this paper is to create a trainable guitar effect pedal that classifies the incoming guitar signal into two or more playing style classes and route the signal to the bypass channel or effect channel depending on the class. A training data set is collected that consists of recorded single notes and power chords. The neural network algorithm is able to distinguish between these two playing styles with 95\% accuracy in the test set. An electronic system is designed with a Raspberry Pi Pico, preamplifiers, multiplexers, and a distortion effect that runs a neural network trained using Edge Impulse software that runs the classification and signal routing in real-time.
