Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks

TL;DR

This paper employs convolutional neural networks to classify and estimate parameters of guitar effects from recordings, introducing a new dataset and demonstrating high accuracy in effect identification and parameter prediction.

Contribution

It presents a novel dataset and demonstrates effective CNN-based classification and parameter estimation for guitar effects, including insights on dataset design choices.

Findings

Classification accuracy exceeds 80%.

Discrete datasets perform as well as continuous ones.

Parameter estimation errors are generally below 0.05.

Abstract

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.