Paper Archive

This paper proposes a grey-box neural network based approach to modelling LFO modulated time-varying effects. The neural network model receives both the unprocessed audio, as well as the LFO signal, as input. This allows complete control over the model’s LFO frequency and shape. The neural networks are trained using guitar audio, which has to be processed by the target effect and also annotated with the predicted LFO signal before training. A measurement signal based on regularly spaced chirps was used to accurately predict the LFO signal. The model architecture has been previously shown to be capable of running in real-time on a modern desktop computer, whilst using relatively little processing power. We validate our approach creating models of both a phaser and a flanger effects pedal, and theoretically it can be applied to any LFO modulated time-varying effect. In the best case, an errorto-signal ratio of 1.3% is achieved when modelling a flanger pedal, and previous work has shown that this corresponds to the model being nearly indistinguishable from the target device.

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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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This paper is concerned with the conception of methods tailored for the numerical simulation of power-balanced systems that are well-posed but implicitly described. The motivation is threefold: some electronic components (such as the ideal diode) can only be implicitly described, arbitrary connection of components can lead to implicit topological constraints, finally stable discretization schemes also lead to implicit algebraic equations. In this paper we start from the representation of circuits using a power-balanced Kirchhoff-Dirac structure, electronic components are described by a local state that is observed through a pair of power-conjugated algebro-differential operators (V, I) to yield the branch voltages and currents, the arc length is used to parametrize switching and non-Lipschitz components, and a power balanced functional time-discretization is proposed. Finally, the method is illustrated on two simple but non-trivial examples.

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Reverberation is one of the most important effects used in audio production. Although nowadays numerous real-time implementations of artificial reverberation algorithms are available, many of them depend on a database of recorded or pre-synthesized room impulse responses, which are convolved with the input signal. Implementations that use an algorithmic approach are more flexible but do not let the users have full control over the produced sound, allowing only a few selected parameters to be altered. The realtime implementation of an artificial reverberation synthesizer presented in this study introduces an audio plugin based on a feedback delay network (FDN), which lets the user have full and detailed insight into the produced reverb. It allows for control of reverberation time in ten octave bands, simultaneously allowing adjusting the feedback matrix type and delay-line lengths. The proposed plugin explores various FDN setups, showing that the lowest useful order for high-quality sound is 16, and that in the case of a Householder matrix the implementation strongly affects the resulting reverberation. Experimenting with delay lengths and distribution demonstrates that choosing too wide or too narrow a length range is disadvantageous to the synthesized sound quality. The study also discusses CPU usage for different FDN orders and plugin states.

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Various audio signal processing applications, such as source separation and dereverberation, require an accurate mathematical modeling of the input audio data. In the literature, many works have focused on source signal modeling, while the reverberation model is often kept very simplistic. This paper aims to investigate a stochastic room impulse response model presented in a previous article: this model is first adapted to discrete time, then we propose a parametric estimation algorithm, that we evaluate experimentally. Our results show that this algorithm is able to efficiently estimate the model parameters, in various experimental settings (various signal-to-noise ratios and absorption coefficients of the room walls).

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The Red Llama guitar overdrive effect pedal differs from most other overdrive effects because it utilizes CMOS inverters, formed by two metal-oxide-semiconductor field-effect transistors (MOSFETs), instead of a combination of operational amplifiers and diodes to obtain nonlinear distortion. This makes it an interesting subject for virtual analog modeling, obviously requiring a suitable model for the CMOS inverters. Therefore, in this paper, we extend a well-known model for MOSFETs by a straight-forward heuristic approach to achieve a good match between the model and measurement data obtained for the individual MOSFETs. This allows a faithful digital simulation of the Red Llama.

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Feedback delay networks (FDNs) are recursive filters, which are widely used for artificial reverberation and decorrelation. While there exists a vast literature on a wide variety of reverb topologies, this work aims to provide a unifying framework to design and analyze delay-based reverberators. To this end, we present the Feedback Delay Network Toolbox (FDNTB), a collection of the MATLAB functions and example scripts. The FDNTB includes various representations of FDNs and corresponding translation functions. Further, it provides a selection of special feedback matrices, topologies, and attenuation filters. In particular, more advanced algorithms such as modal decomposition, time-varying matrices, and filter feedback matrices are readily accessible. Furthermore, our toolbox contains several additional FDN designs. Providing MATLAB code under a GNU-GPL 3.0 license and including illustrative examples, we aim to foster research and education in the field of audio processing.

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Virtual reality applications require all kinds of methods to create plausible virtual acoustics environments to enhance the user experience. Here, we present an acoustic paintbrush method that modifies the timbre of a simple room acoustics simulation with the timbre of a measured room response while aiming to preserve the spatial aspects of the simulated room. In other words, the method only applies the measured spectral coloration and alters the simulated and temporal distribution of early reflections as little as possible. Three variations of the acoustic paintbrush method are validated with a listening test. The results indicate that the method works reasonably well. The paintbrushed room acoustic simulations were perceived to become closer to the measured room acoustics than the source simulation. However, the limits of the perceived effect varied depending on the input signal and the simulated and recorded responses. This warrants for further perceptual testing.

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We propose a new style of continuous-time filter design composed of a cascade of 2nd-order state variable filters (SVFs) and a global feedback path. This family of filters is parameterized by the SVF cutoff frequencies and resonances, as well as the global feedback amount. For the case of two identical SVFs in cascade and a specific value of the SVF resonance, the proposed design reduces to the well-known Moog ladder filter. For another resonance value, it approximates the Octave CAT filter. The resonance parameter can be used to create new filters as well. We study the pole loci and transfer functions of the SVF building block and entire filter. We focus in particular on the effect of the proposed parameterization on important aspects of the filter’s response, including the passband gain and cutoff frequency error. We also present the first in-depth study of the Octave CAT filter circuit.

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In artificial reverb algorithms, gains are commonly varied over time to break up temporal patterns, improving quality. We propose a family of novel Schroeder-style allpass filters that are energypreserving under arbitrary, continuous changes of their gains over time. All of them are canonic in delays, and some are also canonic in multiplies. This yields several structures that are novel even in the time-invariant case. Special cases for cascading and nesting these structures with a reduced number of multipliers are shown as well. The proposed structures should be useful in artificial reverb applications and other time-varying audio effects based on allpass filters, especially where allpass filters are embedded in feedback loops and stability may be an issue.

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