Download A Csound Opcode for a Triode Stage of a Vacuum Tube Amplifier The Csound audio programming language adheres to the inputoutput paradigm and provides a large number of specialized commands (called opcodes) for processing output signals from input signals. Therefore it is not directly suitable for component modeling of analog circuitry. This contribution describes an attempt to virtual analog modeling and presents a Csound opcode for a triode stage of a vacuum tube amplifier. Externally it communicates with other opcodes via input and output signals at the sample rate. Internally it uses an established wave digital filter model of a standard triode. The opcode is available as library module.
Download Soliton Sonification - Experiments with the Kortweg-deVries Equation Solitons are special solutions of certain nonlinear partial differential equations of mathematical physics. They exhibit properties that are partly similar to the solutions of the linear wave equation and partly similar to the behaviour of colliding particles. Their characteristic features are well-known in the mathematical literature but few closed-form solutions are available. This contribution derives algorithmic structures for the computation of solitons in a dimensionless space-time domain which can be scaled to the audio frequency range. The investigations are confined to first and second order solutions of the Korteweg-de Vries equation. Sound examples show that the effects of wave propagation and soliton interaction can be represented by audible events.
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.