Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks
This work provides a method for automatic recognition and parameter estimation of guitar effects, which could benefit musicians and audio engineers by streamlining sound design and analysis.
This paper addresses the gap in research on classifying and estimating parameters of guitar effects from recordings. It uses convolutional neural networks to classify 13 overdrive, distortion, and fuzz effects with over 80% accuracy and estimates their parameters with a mean absolute error mostly below 0.05.
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.