  By processing audio signals in the time-domain with randomly weighted
temporal convolutional networks (TCNs), we uncover a wide range of novel, yet
controllable overdrive effects. We discover that architectural aspects, such as
the depth of the network, the kernel size, the number of channels, the
activation function, as well as the weight initialization, all have a clear
impact on the sonic character of the resultant effect, without the need for
training. In practice, these effects range from conventional overdrive and
distortion, to more extreme effects, as the receptive field grows, similar to a
fusion of distortion, equalization, delay, and reverb. To enable use by
musicians and producers, we provide a real-time plugin implementation. This
allows users to dynamically design networks, listening to the results in
real-time. We provide a demonstration and code at
https://csteinmetz1.github.io/ronn.
