Download Inference-Time Structured Pruning for Real-Time Neural Network Audio Effects Structured pruning is a technique for reducing the computational
load and memory footprint of neural networks by removing structured subsets of parameters according to a predefined schedule
or ranking criterion.
This paper investigates the application of
structured pruning to real-time neural network audio effects, focusing on both feedforward networks and recurrent architectures.
We evaluate multiple pruning strategies at inference time, without retraining, and analyze their effects on model performance. To
quantify the trade-off between parameter count and audio fidelity,
we construct a theoretical model of the approximation error as a
function of network architecture and pruning level. The resulting bounds establish a principled relationship between pruninginduced sparsity and functional error, enabling informed deployment of neural audio effects in constrained real-time environments.
Download Sample Rate Independent Recurrent Neural Networks for Audio Effects Processing In recent years, machine learning approaches to modelling guitar amplifiers and effects pedals have been widely investigated and have become standard practice in some consumer products. In particular, recurrent neural networks (RNNs) are a popular choice for modelling non-linear devices such as vacuum tube amplifiers and distortion circuitry. One limitation of such models is that they are trained on audio at a specific sample rate and therefore give unreliable results when operating at another rate. Here, we investigate several methods of modifying RNN structures to make them approximately sample rate independent, with a focus on oversampling. In the case of integer oversampling, we demonstrate that a previously proposed delay-based approach provides high fidelity sample rate conversion whilst additionally reducing aliasing. For non-integer sample rate adjustment, we propose two novel methods and show that one of these, based on cubic Lagrange interpolation of a delay-line, provides a significant improvement over existing methods. To our knowledge, this work provides the first in-depth study into this problem.