Papers › Randomized Overdrive Neural Networks

Randomized Overdrive Neural Networks

8 Oct 2020arXiv:2010.04237archive 2025-07-28

Christian J. Steinmetz, Joshua D. Reiss

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.

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FiLM csteinmetz1/ronn/dev/ronn/condition.py official repository unverified Apache-2.0 (permissive) · e8a61d0fde7b7205 · report
calculate_receptive_field csteinmetz1/ronn/dev/ronn/utils.py official repository unverified Apache-2.0 (permissive) · 679bec72525ea701 · report
center_crop csteinmetz1/ronn/dev/ronn/utils.py official repository unverified Apache-2.0 (permissive) · f3db0665bb913e94 · report
get_activation csteinmetz1/ronn/dev/ronn/utils.py official repository unverified Apache-2.0 (permissive) · 394d882b23b787d4 · report

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