  Despite the popularity of guitar effects, there is very little existing
research on classification and parameter estimation of specific plugins or
effect units from guitar recordings. In this paper, convolutional neural
networks were used for classification and parameter estimation for 13
overdrive, distortion and fuzz guitar effects. A novel dataset of processed
electric guitar samples was assembled, with four sub-datasets consisting of
monophonic or polyphonic samples and discrete or continuous settings values,
for a total of about 250 hours of processed samples. Results were compared for
networks trained and tested on the same or on a different sub-dataset. We found
that discrete datasets could lead to equally high performance as continuous
ones, whilst being easier to design, analyse and modify. Classification
accuracy was above 80\%, with confusion matrices reflecting similarities in the
effects timbre and circuits design. With parameter values between 0.0 and 1.0,
the mean absolute error is in most cases below 0.05, while the root mean square
error is below 0.1 in all cases but one.
