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.
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 ALAMODE: Automated Learning of Acoustical Modal Parameters via Differential Evolution This paper is a technical report on the methodology submitted for Task A of the 1st DAFx Parameter Estimation Challenge. The goal of the challenge’s task is to invert the multi-dimensional physical and geometric parameters of a virtual plate reverberator given a target reference impulse response. To achieve this, we present a multi-stage gradient-free optimization framework. This three-stage optimization is computed using an efficient physics-based simulator, starting with an optimization of only mode frequency-determining physical parameters, followed by a 6-DoF parameter optimization with position-determining ones and a final phase for frequency- and position-independent mode amplitude estimation.
Download Performance-Oriented Wave Digital Circuit Emulation Wave Digital Filters are a circuit-modeling paradigm well-suited for reusable software implementation, but existing software implementations often incur significant overhead due to run-time abstractions and data layout constraints. This paper presents a performance-oriented toolchain for implementing Wave Digital circuit models based on static code generation. The toolchain consists of a declarative circuit description language, a compiler that generates circuit simulation code with minimal persistent state and no run-time abstraction, and a minimal runtime library implementing specialized circuit components as Wave Digital Filters. Performance measurements across several test circuits demonstrate that the generated models consistently outperform existing implementations, and achieve near-ideal performance relative to a theoretical execution bound.