Paper Archive

This paper introduces a computationally efficient method for the emulation of nonlinear analog audio circuits by combining state-space representations, constraint stabilization, and convex quadratic programming (QP). Unlike traditional virtual analog (VA) modeling approaches or computationally demanding SPICE-based simulations, our approach reformulates the nonlinear differential-algebraic (DAE) systems that arise from analog circuit analysis into numerically stable optimization problems. The proposed method efficiently addresses the numerical challenges posed by nonlinear algebraic constraints via constraint stabilization techniques, significantly enhancing robustness and stability, suitable for real-time simulations. A canonical diode clipper circuit is presented as a test case, demonstrating that our method achieves accurate and faster emulations compared to conventional state-space methods. Furthermore, our method performs very well even at substantially lower sampling rates. Preliminary numerical experiments confirm that the proposed approach offers improved numerical stability and real-time feasibility, positioning it as a practical solution for high-fidelity audio applications.

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Antiderivative Antialiasing (ADAA), has become a pivotal method for reducing aliasing when dealing with nonlinear function at audio rate. However, its implementation requires analytical computation of the antiderivative of the nonlinear function, which in practical cases can be challenging without a symbolic solver. Moreover, when the nonlinear function is given by measurements it must be approximated to get a symbolic description. In this paper, we propose a simple approach to ADAA for practical applications that employs numerical integration of lookup tables (LUTs) to approximate the antiderivative. This method eliminates the need for closed-form solutions, streamlining the ADAA implementation process in industrial applications. We analyze the trade-offs of this approach, highlighting its computational efficiency and ease of implementation while discussing the potential impact of numerical integration errors on aliasing performance. Experiments are conducted with static nonlinearities (tanh, a simple wavefolder and the Buchla 259 wavefolding circuit) and a stateful nonlinear system (the diode clipper).

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Neural networks have become ubiquitous with guitar distortion effects modelling in recent years. Despite their ability to yield perceptually convincing models, they are susceptible to frequency aliasing when driven by high frequency and high gain inputs. Nonlinear activation functions create both the desired harmonic distortion and unwanted aliasing distortion as the bandwidth of the signal is expanded beyond the Nyquist frequency. Here, we present a method for reducing aliasing in neural models via a teacher-student fine tuning approach, where the teacher is a pretrained model with its weights frozen, and the student is a copy of this with learnable parameters. The student is fine-tuned against an aliasing-free dataset generated by passing sinusoids through the original model and removing non-harmonic components from the output spectra. Our results show that this method significantly suppresses aliasing for both long-short-term-memory networks (LSTM) and temporal convolutional networks (TCN). In the majority of our case studies, the reduction in aliasing was greater than that achieved by two times oversampling. One side-effect of the proposed method is that harmonic distortion components are also affected. This adverse effect was found to be modeldependent, with the LSTM models giving the best balance between anti-aliasing and preserving the perceived similarity to an analog reference device.

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In this paper, we present an approach to the neural modeling of overdrive guitar pedals with conditioning from a cross-circuit and cross-setting latent space. The resulting network models the behavior of multiple overdrive pedals across different settings, offering continuous morphing between real configurations and hybrid behaviors. Compact conditioning spaces are obtained through unsupervised training of a variational autoencoder with adversarial training, resulting in accurate reconstruction performance across different sets of pedals. We then compare three Hyper-Recurrent architectures for processing, including dynamic and static HyperRNNs, and a smaller model for real-time processing. Additionally, we present pOD-set, a new open dataset including recordings of 27 analog overdrive pedals, each with 36 gain and tone parameter combinations totaling over 97 hours of recordings. Precise parameter setting was achieved through a custom-deployed recording robot.

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Most real systems, from acoustics to analog electronics, are characterised by bidirectional coupling amongst elements rather than neat, unidirectional signal flows between self-contained modules. Integrating digital processing into physical domains becomes a significant engineering challenge when the application requires bidirectional coupling across the physical-digital boundary rather than separate, well-defined inputs and outputs. We introduce an approach to hybrid analog-digital audio processing using synthetic impedance: digitally simulated circuit elements integrated into an otherwise analog circuit. This approach combines the physicality and classic character of analog audio circuits alongside the precision and flexibility of digital signal processing (DSP). Our impedance synthesis system consists of a voltage-controlled current source and a microcontroller-based DSP system. We demonstrate our technique through modifying an iconic guitar distortion pedal, the Boss DS-1, showing the ability of the synthetic impedance to both replicate and extend the behaviour of the pedal’s diode clipping stage. We discuss the behaviour of the synthetic impedance in isolated laboratory conditions and in the DS-1 pedal, highlighting the technical and creative potential of the technique as well as its practical limitations and future extensions.

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Neural networks have become invaluable for general audio processing tasks, such as virtual analog modeling of nonlinear audio equipment. For sequence modeling tasks in particular, recurrent neural networks (RNNs) have gained widespread adoption in recent years. Their general applicability and effectiveness stems partly from their inherent nonlinearity, which makes them prone to aliasing. Recent work has explored mitigating aliasing by oversampling the network—an approach whose effectiveness is directly linked with the incurred computational costs. This work explores an alternative route by extending the antiderivative antialiasing technique to explicit, computable RNNs. Detailed applications to the Gated Recurrent Unit and Long Short-Term Memory cell are shown as case studies. The proposed technique is evaluated on multiple pre-trained guitar amplifier models, assessing its impact on the amount of aliasing and model tonality. The method is shown to reduce the models’ tendency to alias considerably across all considered sample rates while only affecting their tonality moderately, without requiring high oversampling factors. The results of this study can be used to improve sound quality in neural audio processing tasks that employ a suitable class of RNNs. Additional materials are provided in the accompanying webpage.

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Machine learning models have become ubiquitous in modeling analog audio devices. Expanding on this line of research, our study focuses on Voltage-Controlled Oscillators of analog synthesizers. We employ black box autoregressive artificial neural networks to model the typical analog waveshapes, including triangle, square, and sawtooth. The models can be conditioned on wave frequency and type, enabling the generation of pitch envelopes and morphing across waveshapes. We conduct evaluations on both synthetic and analog datasets to assess the accuracy of various architectural variants. The LSTM variant performed better, although lower frequency ranges present particular challenges.

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Virtual Analog (VA) modeling aims to simulate the behavior of hardware circuits via algorithms to replicate their tone digitally. Dynamic Range Compressor (DRC) is an audio processing module that controls the dynamics of a track by reducing and amplifying the volumes of loud and quiet sounds, which is essential in music production. In recent years, neural-network-based VA modeling has shown great potential in producing high-fidelity models. However, due to the lack of data quantity and diversity, their generalization ability in different parameter settings and input sounds is still limited. To tackle this problem, we present Solid State Bus-Comp, the first large-scale and diverse dataset for modeling the classical VCA compressor — SSL 500 G-Bus. Specifically, we manually collected 175 unmastered songs from the Cambridge Multitrack Library. We recorded the compressed audio in 220 parameter combinations, resulting in an extensive 2528-hour dataset with diverse genres, instruments, tempos, and keys. Moreover, to facilitate the use of our proposed dataset, we conducted benchmark experiments in various open-sourced black-box and grey-box models, as well as white-box plugins. We also conducted ablation studies in different data subsets to illustrate the effectiveness of the improved data diversity and quantity. The dataset and demos are on our project page: https: //www.yichenggu.com/SolidStateBusComp/.

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Some early analog voltage-controlled amplifiers (VCAs) utilized semiconductor diodes as a variable-gain element. Diode-based VCAs exhibit a unique sound quality, with distortion dependent both on signal level and gain control. In this work, we examine the behavior of a simplified circuit for a diode-based VCA and propose a nonlinear, explicit, stateless digital model. This approach avoids traditional iterative algorithms, which can be computationally intensive. The resulting digital model retains the sonic characteristics of the analog model and is suitable for real-time simulation. We present an analysis of the gain characteristics and harmonic distortion produced by this model, as well as practical guidance for implementation. We apply this approach to a set of alternative analog topologies and introduce a family of digital VCA models based on fixed nonlinearities with variable operating points.

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Several methods exist in the literature to accurately simulate Bucket Brigade Device (BBD) chips, which are widely used in analog delay-based audio effects for their characteristic lo-fi sound, which is affected by noise, nonlinearities and aliasing. The latter is a desired quality, being typical of those chips. However, when simulating BBDs in a discrete-time domain environment, additional aliasing components occur that need to be suppressed. In this work, we propose a novel method that applies the Bandlimited Step (BLEP) technique, effectively minimizing aliasing artifacts introduced by the simulation. The paper provides some insights on the design of a BBD simulation using interpolation at the input for clock rate conversion and, most importantly, shows how BLEP can be effective in reducing unwanted aliasing artifacts. Interpolation is shown to have minor importance in the reduction of spurious components.

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