Blind Extraction of Guitar Effects Through Blind System Inversion and Neural Guitar Effect Modelling

Reemt Hinrichs, Kevin Gerkens, Jörn Ostermann; EURASIP 2023 (under review)
Correspondence: hinrichs@tnt.uni-hannover.de

Abstract:
Audio effects are an ubiquitous tool in music production due to the interesting ways in which they can shape the sound of music. Guitar effects, the subset of all audio effects focusing on guitar signals, 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. Automatic extraction of guitar effects and their parameter settings has been investigated in previous research, where artificial neural networks determine the effect class of a reference signal, and subsequently the parameter settings. However, to actually make use of these extracted information, a corresponding guitar effect implementation has to be available. To circumvent this issue, we propose blind extraction of guitar effects through a combination of blind system inversion and neural guitar effect modelling. That way, an immediately usable, blind copy of the target guitar effect is obtained. The proposed method is tested with the Phaser, Softclipping and Slapback Delay effect. Listening tests with eight subjects indicate excellent quality of the the blind copies, i.e. little to no difference to the reference guitar effect.