Guitar effects are commonly used in popular music to shape the
guitar sound to fit specific genres or to create more variety within
musical compositions. The sound is not only determined by the
choice of the guitar effect, but also heavily depends on the parameter settings of the effect. This paper introduces a method to
estimate the parameter settings of guitar effects, which makes it
possible to reconstruct the effect and its settings from an audio
recording of a guitar. The method utilizes audio feature extraction and shallow neural networks, which are trained on data created specifically for this task. The results show that the method
is generally suited for this task with average estimation errors of
±5% − ±16% of different parameter scales and could potentially
perform near the level of a human expert.