Paper Archive

In this study, a famous boxed effect pedal, also called stompbox, for electrical guitars is analyzed and simulated. The nodal DK method is used to create a non-linear state-space system with Matlab as a physical model for the MXR Phase 90 guitar effect pedal. A crucial component of the effect are Junction Field Effect Transistors (JFETs) which are used as variable resistors to dynamically vary the phase-shift characteristic of an allpass-filter cascade. So far, virtual analog modeling in the context of audio has mainly been applied to diode-clippers and vacuum tube circuits. This work shows an efficient way of describing the nonlinear behavior of JFETs, which are wide-spread in audio devices. To demonstrate the applicability of the proposed physical model, a real-time VST audio plug-in was implemented.

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Reusing recorded sounds (sampling) is a key component in Electronic Music Production (EMP), which has been present since its early days and is at the core of genres like hip-hop or jungle. Commercial and non-commercial services allow users to obtain collections of sounds (sample packs) to reuse in their compositions. Automatic classification of one-shot instrumental sounds allows automatically categorising the sounds contained in these collections, allowing easier navigation and better characterisation. Automatic instrument classification has mostly targeted the classification of unprocessed isolated instrumental sounds or detecting predominant instruments in mixed music tracks. For this classification to be useful in audio databases for EMP, it has to be robust to the audio effects applied to unprocessed sounds. In this paper we evaluate how a state of the art model trained with a large dataset of one-shot instrumental sounds performs when classifying instruments processed with audio effects. In order to evaluate the robustness of the model, we use data augmentation with audio effects and evaluate how each effect influences the classification accuracy.

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This research presents a model of the avian vocal tract, implemented using classical waveguide synthesis and numerical methods. The vocal organ of the songbird, the syrinx, has a unique topography of acoustic tubes (a trachea with a bifurcation at its base) making it a rather unique subject for waveguide synthesis. In the upper region of the two bifid bronchi lies a nonlinear vibrating membrane – the primary resonator in sound production. Unlike most reed musical instruments, the more significant displacement of the membrane is perpendicular to the directions of airflow, due to the Bernoulli effect. The model of the membrane displacement, and the resulting pressure through the constriction created by the membrane motion, is therefore derived beginning with the Bernoulli equation.

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The real-time simulation of analog circuits by digital systems becomes problematic when parametric components like potentiometers are involved. In this case the coefficients defining the digital system will change and have to be adapted. One common solution is to recalculate the coefficients in real-time, a possibly computationally expensive operation. With a view to the simulation using state-space representations, two parametric subcircuits found in typical guitar amplifiers are analyzed, namely the tone stack, a linear passive network used as simple equalizer and a distorting preamplifier, limiting the signal amplitude with LEDs. Solutions using trapezoidal rule discretization are presented and discussed. It is shown, that the computational costs in case of recalculation of the coefficients are reduced compared to the related DK-method, due to minimized matrix formulations. The simulation results are compared to reference data and show good match.

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A major problem in the emulation of discrete-time nonlinear systems, such as those encountered in Virtual Analog modeling, is aliasing distortion. A trivial approach to reduce aliasing is oversampling. However, this solution may be too computationally demanding for real-time applications. More advanced techniques to suppress aliased components are arbitrary-order Antiderivative Antialiasing (ADAA) methods that approximate the reference nonlinear function using a combination of its antiderivatives of different orders. While in its original formulation it is applied only to memoryless systems, recently, the applicability of first-order ADAA has been extended to stateful systems employing their statespace description. This paper presents an alternative formulation that successfully applies arbitrary-order ADAA methods to Wave Digital Filter models of dynamic circuits with one nonlinear element. It is shown that the proposed approach allows us to design ADAA models of the nonlinear elements in a fully local and modular fashion, independently of the considered reference circuit. Further peculiar features of the proposed approach, along with two examples of applications, are discussed.

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In this paper, we show how to expand the class of audio circuits that can be modeled using Wave Digital Filters (WDFs) to those involving operational transconductance amplifiers (OTAs). Two types of behavioral OTA models are presented and both are shown to be compatible with the WDF approach to circuit modeling. As a case study, an envelope filter guitar effect based around OTAs is modeled using WDFs. The modeling results are shown to be accurate when to compared to state of the art circuit simulation methods.

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In this paper a new technique is introduced that allows for the variable step-size simulation of wave digital filters. The technique is based on the preservation of the underlying network variables which prevents fluctuation in the stored energy in reactive network elements when the step-size is changed. This method allows for the step-size variation of wave digital filters discretized with any passive discretization technique and works with both linear and nonlinear reference circuits. The usefulness of the technique with regards to audio circuit simulation is demonstrated via the case study of a relaxation oscillator where it is shown how the variable step-size technique can be used to mitigate frequency error that would otherwise occur with a fixed step-size simulation. Additionally, an example of how aliasing suppression techniques can be combined with physical modeling is given with an example of the polyBLEP antialiasing technique being applied to the output voltage signal of the relaxation oscillator.

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In this paper, we propose the use of real-valued Linear Recurrent Units (LRUs) for black-box modeling of audio circuits. A network architecture composed of real LRU blocks interleaved with nonlinear processing stages is proposed. Two case studies are presented, a second-order diode clipper and an overdrive distortion pedal. Furthermore, we show how to integrate the antiderivative antialiaisng technique into the proposed method, effectively lowering oversampling requirements. Our experiments show that the proposed method generates models that accurately capture the nonlinear dynamics of the examined devices and are highly efficient, which makes them suitable for real-time operation inside Digital Audio Workstations.

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In this paper, we address automating and systematizing the process of finding black-box models for virtual analogue audio effects with an optimal balance between error and latency. We introduce a multi-objective optimization approach based on hardware-aware neural architecture search which allows specifying the optimization balance of model error and latency according to the requirements of the application. By using a regularized evolutionary algorithm, it is able to navigate through a huge search space systematically. Additionally, we propose a search space for modelling non-linear dynamic audio effects consisting of over 41 trillion different WaveNet-style architectures. We evaluate its performance and usefulness by yielding highly effective architectures, either up to 18× faster or with a test loss of up to 56% less than the best performing models of the related work, while still showing a favourable trade-off. We can conclude that hardware-aware neural architecture search is a valuable tool that can help researchers and engineers developing virtual analogue models by automating the architecture design and saving time by avoiding manual search and evaluation through trial-and-error.

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Physical modelling sound synthesis is a powerful method for constructing virtual instruments aiming to mimic the sound of realworld counterparts, while allowing for the possibility of engaging with these instruments in ways which may be impossible in person. Such a case is explored in this paper: particularly the simulation of a friction drum inspired instrument. It is an instrument played by causing the membrane of a drum head to vibrate via friction. This involves rubbing the membrane via a stick or a cord attached to its center, with the induced vibrations being transferred to the air inside a sound box. This paper describes the development of a real-time audio application which models such an instrument as a bowed membrane connected to an acoustic tube. This is done by means of a numerical simulation using finite-difference time-domain (FDTD) methods in which the excitation, whose position is free to change in real-time, is modelled by a highly non-linear elasto-plastic friction model. Additionally, the virtual instrument allows for dynamically modifying physical parameters of the model, thereby allowing the user to generate new and interesting sounds that go beyond a realworld friction drum.

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