Throughout audio technology history, engineers, circuit designers, and guitarists have searched tirelessly for novel, extreme, and exciting effects as a result of clipping audio signals. Whether it be vacuum tubes (valves), diodes, transistors, op-amps, microchips, or broken speaker drivers doing the distorting, it seems that we have tried them all. But maybe there is at least one area left relatively under-explored, and thats the realm of neural networks.
Now neural networks, have been a round for a bit. They have actually ALREADY been used to model distortion and overdrive effects from guitar amplifier and pedals quite a bit (such as here, here, here, and here). So then you may be asking, "well how is this any different?" And the answer is, ronn doesn't model ANY pre-existing audio circuit, we don't even bother to train anything! Instead we treat the concept of the neural network as a system which can distort a signal, and then we give the user control over that system to explore new effects. Get your hands dirty building neural networks without even touching TensorFlow or PyTorch.
We supply pre-built VST/AU plugins here.
Once downloaded, unzip and move to:
AU: Macintosh HD/Library/Audio/Plug-Ins/Components
VST3: Macintosh HD/Library/Audio/Plug-Ins/VST3
Currently, we only have macOS builds.
You can also build from source. The following steps are for building on macOS, assuming you have XCode and the Command Line Tools, as well as CMake already installed. The steps should be similar for other platforms.
git submodule init
git submodule update
cd plugin/
.zip
file containing the libtorch (PyTorch C++ API) source.
wget https://download.pytorch.org/libtorch/cpu/libtorch-macos-1.7.1.zip
unzip libtorch-macos-1.7.1.zip
cmake -Bbuild -GXcode "-DCMAKE_OSX_ARCHITECTURES==i386;x86_64"
cmake --build build --target ronn_AU ronn_VST3 --config Release
cp -r build/ronn_artefacts/Release/AU/ronn.component "/Volumes/Macintosh HD/Library/Audio/Plug-Ins/Components"
cp -r build/ronn_artefacts/Release/VST3/ronn.vst3 "/Volumes/Macintosh HD/Library/Audio/Plug-Ins/VST3"
The ronn plugin enables users to run their audio directly through randomly weighted temporal convolutional networks (TCNs). Interestingly, using networks that have not been trained can produce a wide range of compelling audio effects simply by adjusting the architectural elements. These effects range from subtle distortion and overdrive, to more extreme drone-like and glitch effects.
@inproceedings{steinmetz2020overdrive,
title={Randomized Overdrive Neural Networks},
author={Steinmetz, Christian J. and Reiss, Joshua D.},
booktitle={4th Workshop on Machine Learning for Creativity and Design at NeurIPS 2020},
year={2020}}