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.
翻译:通过处理时空的音频信号以及随机加权时间变速网络(TCNs),我们发现了一系列新颖的、但可控制的过度驱动效应。我们发现,建筑方面,例如网络深度、内核大小、频道数量、启动功能以及重量初始化,都对由此产生的效应的音频特性产生明显影响,而无需培训。在实践上,这些效应包括常规的过度驱动和扭曲,以及更极端的影响,随着接受场的扩大,类似于扭曲、均衡、延迟和回动的融合。为了让音乐家和制作者能够使用,我们提供了实时插件。这让用户能够动态设计网络,实时聆听结果。我们在 https://csteinetz1.github.io/ronn 上提供了演示和代码。