In today's technologically-enhanced world, just knowing how to play a guitar isn't quite enough (though it is cool). To really get the most out of an electric axe, you gotta know a thing or two about sound theory, and that includes the proper use of an effects pedal to get all those cool wah-wahs squeedly-dees. To properly use an effects pedal, it needs to be carefully programmed ahead of time, then manually activated with one's foot at the proper part of a song. If you're not much of a multitasker, perhaps you need a little assistant to handle the modulation while you focus on playing the actual guitar. Programmer and guitarist Keith Bloemer developed his latest pet project with that precise goal in mind: a Raspberry Pi-powered AI guitar effects pedal.

This is the last of a five-part series on using neural networks for real-time audio. In this article we will go step-by-step to build a functional guitar pedal running neural nets in real-time on the Raspberry Pi. We have now covered three different neural network models and implemented them in real-time guitar plugins with the JUCE framework. As a guitarist and engineer, the next logical step for me is to build a guitar pedal using what we have learned from the previous articles. Amazingly, all the software tools are available for free, and the hardware costs around $150 (less if you already have the required power and audio adapters handy).