Download Real-Time Physical Modelling For Analog Tape Machines For decades, analog magnetic tape recording was the most popular method for recording music, but has been replaced over the past 30 years first by DAT tape, then by DAWs and audio interfaces. Despite being replaced by higher quality technology, many have sought to recreate a "tape" sound through digital effects, despite the distortion, tape "hiss", and other oddities analog tape produced. The following paper describes the general process of creating a physical model of an analog tape machine starting from basic physical principles, then discusses in-depth a real-time implementation of a physical model of a Sony TC-260 tape machine."Whatever you now find weird, ugly, uncomfortable, and nasty about a new medium will surely become its signature. CD distortion, the jitteriness of digital video, the crap sound of 8-bit - all of these will be cherished and emulated as soon as they can be avoided." -Brian Eno.
Download Stable Structures for Nonlinear Biquad Filters Biquad filters are a common tool for filter design. In this writing,
we develop two structures for creating biquad filters with nonlinear elements. We provide conditions for the guaranteed stability of
the nonlinear filters, and derive expressions for instantaneous pole
analysis. Finally, we examine example filters built with these nonlinear structures, and show how the first nonlinear structure can be
used in the context of analog modelling.
Download Water Bottle Synthesis With Modal Signal Processing We present a method for accurately synthesizing the acoustic response of a water bottle using modal signal processing. We start
with extensive measurements of two water bottles with considerations for how the level of water inside the bottles, the area covered
by stickers attached to the exterior of the bottles, and the method
of striking the bottles affect their sound. We perform modal analysis of these measurements and implement a real-time modal water
bottle synthesizer.
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.