Download Non-Iterative Schemes for the Simulation of Nonlinear Audio Circuits In this work, a number of numerical schemes are presented in the
context of virtual-analog simulation. The schemes are linearlyimplicit in character, and hence directly solvable without iterative
methods. Schemes of increasing order of accuracy are constructed,
and convergence and stability conditions are proven formally. The
schemes are able to handle stiff problems very efficiently, because
of their fast update, and can be run at higher sample rates to reduce
aliasing. The cases of the diode clipper and ring modulator are
investigated in detail, including several numerical examples.
Download Applications of Port Hamiltonian Methods to Non-Iterative Stable Simulations of the Korg35 and Moog 4-Pole Vcf This paper presents an application of the port Hamiltonian formalism to the nonlinear simulation of the OTA-based Korg35 filter circuit and the Moog 4-pole ladder filter circuit. Lyapunov analysis is
used with their state-space representations to guarantee zero-input
stability over the range of parameters consistent with the actual
circuits. A zero-input stable non-iterative discrete-time scheme
based on a discrete gradient and a change of state variables is
shown along with numerical simulations. Simulations show behavior consistent with the actual operation of the circuits, e.g.,
self-oscillation, and are found to be stable and have lower computational cost compared to iterative methods.
Download A Physical Model of the Trombone Using Dynamic Grids for Finite-Difference Schemes In this paper, a complete simulation of a trombone using finitedifference time-domain (FDTD) methods is proposed. In particular, we propose the use of a novel method to dynamically vary the
number of grid points associated to the FDTD method, to simulate
the fact that the physical dimension of the trombone’s resonator
dynamically varies over time. We describe the different elements
of the model and present the results of a real-time simulation.
Download Dynamic Grids for Finite-Difference Schemes in Musical Instrument Simulations For physical modelling sound synthesis, many techniques are available; time-stepping methods (e.g., finite-difference time-domain
(FDTD) methods) have an advantage of flexibility and generality
in terms of the type of systems they can model. These methods do,
however, lack the capability of easily handling smooth parameter
changes while retaining optimal simulation quality and stability,
something other techniques are better suited for. In this paper,
we propose an efficient method to smoothly add and remove grid
points from a FDTD simulation under sub-audio rate parameter
variations. This allows for dynamic parameter changes in physical models of musical instruments. An instrument such as the
trombone can now be modelled using FDTD methods, as well as
physically impossible instruments where parameters such as e.g.
material density or its geometry can be made time-varying. Results show that the method does not produce (visible) artifacts and
stability analysis is ongoing.
Download Efficient simulation of the yaybahar using a modal approach This work presents a physical model of the yaybahar, a recently invented acoustic instrument. Here, output from a bowed string is passed through a long spring, before being amplified and propagated in air via a membrane. The highly dispersive character of the spring is responsible for the typical synthetic tonal quality of this instrument. Building on previous literature, this work presents a modal discretisation of the full system, with fine control over frequency-dependent decay times, modal amplitudes and frequencies, all essential for an accurate simulation of the dispersive characteristics of reverberation. The string-bow-bridge system is also solved in the modal domain, using recently developed noniterative numerical methods allowing for efficient simulation.
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 Differentiable grey-box modelling of phaser effects using frame-based spectral processing Machine learning approaches to modelling analog audio effects have seen intensive investigation in recent years, particularly in the context of non-linear time-invariant effects such as guitar amplifiers. For modulation effects such as phasers, however, new challenges emerge due to the presence of the low-frequency oscillator which controls the slowly time-varying nature of the effect. Existing approaches have either required foreknowledge of this control signal, or have been non-causal in implementation. This work presents a differentiable digital signal processing approach to modelling phaser effects in which the underlying control signal and time-varying spectral response of the effect are jointly learned. The proposed model processes audio in short frames to implement a time-varying filter in the frequency domain, with a transfer function based on typical analog phaser circuit topology. We show that the model can be trained to emulate an analog reference device, while retaining interpretable and adjustable parameters. The frame duration is an important hyper-parameter of the proposed model, so an investigation was carried out into its effect on model accuracy. The optimal frame length depends on both the rate and transient decay-time of the target effect, but the frame length can be altered at inference time without a significant change in accuracy.
Download Differentiable All-Pole Filters for Time-Varying Audio Systems Infinite impulse response filters are an essential building block of many time-varying audio systems, such as audio effects and synthesisers. However, their recursive structure impedes end-toend training of these systems using automatic differentiation. Although non-recursive filter approximations like frequency sampling and frame-based processing have been proposed and widely used in previous works, they cannot accurately reflect the gradient of the original system. We alleviate this difficulty by reexpressing a time-varying all-pole filter to backpropagate the gradients through itself, so the filter implementation is not bound to the technical limitations of automatic differentiation frameworks. This implementation can be employed within audio systems containing filters with poles for efficient gradient evaluation. We demonstrate its training efficiency and expressive capabilities for modelling real-world dynamic audio systems on a phaser, time-varying subtractive synthesiser, and feed-forward compressor. We make our code and audio samples available and provide the trained audio effect and synth models in a VST plugin1 .
Download Real-Time Modal Synthesis of Nonlinearly Interconnected Networks Modal methods are a long-established approach to physical modeling sound synthesis. Projecting the equation of motion of a linear, time-invariant system onto a basis of eigenfunctions yields a set of independent forced, lossy oscillators, which may be simulated efficiently and accurately by means of standard time-stepping methods. Extensions of modal techniques to nonlinear problems are possible, though often requiring the solution of densely coupled nonlinear time-dependent equations. Here, an application of recent results in numerical simulation design is employed, in which the nonlinear energy is first quadratised via a convenient auxiliary variable. The resulting equations may be updated in time explicitly, thus avoiding the need for expensive iterative solvers, dense linear system solutions, or matrix inversions. The case of a network of interconnected distributed elements is detailed, along with a real-time implementation as an audio plugin.
Download Interpolation Filters for Antiderivative Antialiasing Aliasing is an inherent problem in nonlinear digital audio processing which results in undesirable audible artefacts. Antiderivative antialiasing has proved to be an effective approach to mitigate aliasing distortion, and is based on continuous-time convolution of a linearly interpolated distorted signal with antialiasing filter kernels. However, the performance of this method is determined by the properties of interpolation filter. In this work, cubic interpolation kernels for antiderivative antialiasing are considered. For memoryless nonlinearities, aliasing reduction is improved employing cubic interpolation. For stateful systems, numerical simulation and stability analysis with respect to different interpolation kernels remain in favour of linear interpolation.