Randomized Overdrive Neural Networks
This work addresses the need for creative audio effects tools for musicians and producers, offering a novel approach to sound design, though it is incremental in applying existing neural network architectures to a new domain.
The paper tackled the problem of generating novel and controllable overdrive effects for audio processing by using randomly weighted temporal convolutional networks (TCNs) without training, resulting in effects ranging from conventional distortion to extreme combinations like distortion, equalization, delay, and reverb, with a real-time plugin implementation provided for practical use.
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.