Digital audio effects are used by many electric guitar players. These effects help players to find their desired tones and sounds. For the modelling of main nonlinear guitar effects, distortion and overdrive, this paper investigates current methods of static modelling and dynamic nonlinear state space solutions. After discussion of previous models, this paper introduces a new method of distortion modelling with system identification called Enhanced Modelling of Guitar Distortion. Enhanced Modelling of Guitar Distortion algorithm will use adaptive network based system identification method ANFIS (Adaptive-Network-Based Fuzzy Inference System).
ANFIS is used as a system identification tool in Enhanced Modelling of Guitar Distortion algorithm. This algorithm takes the guitar output signal and pre-amplifies the input with 12AX7 vacuum tube amplifier simulation model to obtain clean channel. ANFIS System Identification block is trained using desired distortion effect input output pair. This training and learning results into a ANFIS structure that can be used for processing future inputs. Using clean channel output as an input to the ANFIS structure, lead channel output is obtained. Real-time implementations of distortion and overdrive will be done using C/C++/C# software languages. Using different distortion effect input output pair, different models of distortion effects can be obtained.