论文标题
随机超速神经网络
Randomized Overdrive Neural Networks
论文作者
论文摘要
通过使用随机加权的时间卷积网络（TCN）处理时间域中的音频信号，我们发现了广泛但可控制的超速效应。我们发现架构方面，例如网络的深度，内核大小，通道数，激活函数以及权重初始化，都对所得效果的声音特征产生了明显的影响，而无需训练。实际上，这些影响范围从常规的超速和失真到更极端的影响，随着接收场的增长，类似于失真，均衡，延迟和混响的融合。为了使用音乐家和制作人的使用，我们提供了实时插件实现。这使用户可以动态设计网络，实时聆听结果。我们在https://csteinmetz1.github.io/ronn提供了演示和代码。
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.
