Download Physical Modeling Using Recurrent Neural Networks with Fast Convolutional Layers Discrete-time modeling of acoustic, mechanical and electrical systems is a prominent topic in the musical signal processing literature. Such models are mostly derived by discretizing a mathematical model, given in terms of ordinary or partial differential equations, using established techniques. Recent work has applied the techniques of machine-learning to construct such models automatically from data for the case of systems which have lumped states described by scalar values, such as electrical circuits. In this work, we examine how similar techniques are able to construct models of systems which have spatially distributed rather than lumped states. We describe several novel recurrent neural network structures, and show how they can be thought of as an extension of modal techniques. As a proof of concept, we generate synthetic data for three physical systems and show that the proposed network structures can be trained with this data to reproduce the behavior of these systems.
Download A Study of Control Methods for Percussive Sound Synthesis Based on Gans The process of creating drum sounds has seen significant evolution in the past decades. The development of analogue drum synthesizers, such as the TR-808, and modern sound design tools in Digital Audio Workstations led to a variety of drum timbres that defined entire musical genres. Recently, drum synthesis research has been revived with a new focus on training generative neural networks to create drum sounds. Different interfaces have previously been proposed to control the generative process, from low-level latent space navigation to high-level semantic feature parameterisation, but no comprehensive analysis has been presented to evaluate how each approach relates to the creative process. We aim to evaluate how different interfaces support creative control over drum generation by conducting a user study based on the Creative Support Index. We experiment with both a supervised method that decodes semantic latent space directions and an unsupervised Closed-Form Factorization approach from computer vision literature to parameterise the generation process and demonstrate that the latter is the preferred means to control a drum synthesizer based on the StyleGAN2 network architecture.
Download Differentiable White-Box Virtual Analog Modeling Component-wise circuit modeling, also known as “white-box”
modeling, is a well established and much discussed technique in
virtual analog modeling. This approach is generally limited in accuracy by lack of access to the exact component values present in
a real example of the circuit. In this paper we show how this problem can be addressed by implementing the white-box model in a
differentiable form, and allowing approximate component values
to be learned from raw input–output audio measured from a real
device.
Download Combining Zeroth and First-Order Analysis With Lagrange Polynomials to Reduce Artefacts in Live Concatenative Granulation This paper presents a technique addressing signal discontinuity and concatenation artefacts in real-time granular processing
with rectangular windowing. By combining zero-crossing synchronicity, first-order derivative analysis, and Lagrange polynomials, we can generate streams of uncorrelated and non-overlapping
sonic fragments with minimal low-order derivatives discontinuities. The resulting open-source algorithm, implemented in the
Faust language, provides a versatile real-time software for dynamical looping, wavetable oscillation, and granulation with reduced artefacts due to rectangular windowing and no artefacts
from overlap-add-to-one techniques commonly deployed in granular processing.