This repository contains a digital model of the Klon Centaur guitar pedal. The goal is to use this circuit as a test bench for comparing/combining nodal analysis, wave digital filters, and neural network circuit modelling approaches. The model is implemented as an audio plugin (Standalone/VST/AU), and as a guitar pedal-style effect embedded on a Teensy microcontroller.

A full technical paper summarizing this project is available on the ArXiv. Original circuit schematic and analysis on ElectroSmash.

This work began as part of a class project for EE 292D at Stanford University.

Plugin builds can be downloaded from the releases page. Check out the video demo on YouTube. Linux users can find builds available on the Open Build Service, courtesy of Konstantin Voinov.

To build the audio plugin, you must have CMake installed (version 3.15 or greater). Then use the following steps:

# clone repository
$ git clone https://github.com/jatinchowdhury18/KlonCentaur.git
$ cd KlonCentaur
$ git submodule update --init --recursive

# Build with CMake
$ cmake -Bbuild
$ cmake --build build/ --config Release

If you also want to build the sub-circuits and performance benchmarking app included in this repo, using the following as your first CMake command: cmake -Bbuild -DBUILD_SUB_CIRCUITS=ON -DBUILD_CENTAUR_BENCH=ON.

The neural network inferencing engine used by the plugin has two implementations, one using the Eigen linear algebra library, and a second using only the C++ standard library (STL). The Eigen implementation is enabled by default, but if you would prefer to use the STL implementation, comment out the following line in CMakeLists.txt:

# comment to use STL implementation instead of Eigen
add_definitions(-DUSE_EIGEN)

Check out the video demo on YouTube! For more information on the Teensy pedal-style implementation, see the TeensyCentaur/ subfolder.

The circuit model is constructed using nodal analysis and wave digital filters. For more information see:

• Julius Smith, Physical Audio Signal Processing
• Kurt Werner, Virtual Analog Modelling of Audio Circuitry Using Wave Digital Filters

The wave digital filters are implemented using a WDF library, available here.

In the neural network version of the emulation, a recurrent neural network is used to emulate the gain stage circuit of the original pedal. The RNN architecture used is derived from the one presented by Wright et. al. in their 2019 DAFx paper "Real-Time Black-Box Modelling with Recurrent Neural Networks". Training data consists of ~4 minutes of Direct In (DI) recordings of electric guitar, chopped into 0.5 second segments. The data is then processed through a SPICE model to create a "ground truth" version of the effect to train against. The training data, SPICE model, and Python code for training the networks can be found in the GainStageML/ subfolder.

This repository is licensed under the BSD-3-Clause license. Enjoy!