Classification of Guitar Effects and Extraction of Their Parameter Settings from Instrument Mixes Using Convolutional Neural Networks

Abstract

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. Previous research focused on the classification of guitar effects and extraction of their parameter settings from solo guitar audio recordings. However, more realistic is the classification and extraction from instrument mixes. This work investigates the use of convolution neural networks (CNNs) for classification and extraction of guitar effects from audio samples containing guitar, bass, keyboard and drums. The CNN was compared to baseline methods previously proposed like support vector machines and shallow neural networks together with predesigned features. The CNN outperformed all baselines, achieving a classification accuracy of up to 97.4% and a mean absolute parameter extraction error of below 0.016 for the distortion, below 0.052 for the tremolo and below 0.038 for the slapback delay effect achieving or surpassing the presumed human expert error of 0.05.