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Despite the popularity of guitar effects, there is very little existingresearch on classification and parameter estimation of specific plugins oreffect units from guitar recordings. In this paper, convolutional neuralnetworks were used for classification and parameter estimation for 13overdrive, distortion and fuzz guitar effects. A novel dataset of processedelectric guitar samples was assembled, with four sub-datasets consisting ofmonophonic or polyphonic samples and discrete or continuous settings values,for a total of about 250 hours of processed samples. Results were compared fornetworks trained and tested on the same or on a different sub-dataset. We foundthat discrete datasets could lead to equally high performance as continuousones, whilst being easier to design, analyse and modify. Classificationaccuracy was above 80\%, with confusion matrices reflecting similarities in theeffects 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 squareerror is below 0.1 in all cases but one.

Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks