Download Explicit Vector Wave Digital Filter Modeling of Circuits with a Single Bipolar Junction Transistor The recently developed extension of Wave Digital Filters based on vector wave variables has broadened the class of circuits with linear two-port elements that can be modeled in a modular and explicit fashion in the Wave Digital (WD) domain. In this paper, we apply the vector definition of wave variables to nonlinear twoport elements. In particular, we present two vector WD models of a Bipolar Junction Transistor (BJT) using characteristic equations derived from an extended Ebers-Moll model. One, implicit, is based on a modified Newton-Raphson method; the other, explicit, is based on a neural network trained in the WD domain and it is shown to allow fully explicit implementation of circuits with a single BJT, which can be executed in real time.
Download A Computational Model of the Hammond Organ Vibrato/Chorus using Wave Digital Filters We present a computational model of the Hammond tonewheel organ vibrato/chorus, a musical audio effect comprising an LC ladder circuit and an electromechanical scanner. We model the LC ladder using the Wave Digital Filter (WDF) formalism, and introduce a new approach to resolving multiple nonadaptable linear elements at the root of a WDF tree. Additionally we formalize how to apply the well-known warped Bilinear Transform to WDF discretization of capacitors and inductors and review WDF polarity inverters. To model the scanner we propose a simplified and physically-informed approach. We discuss the time- and frequency-domain behavior of the model, emphasizing the spectral properties of interpolation between the taps of the LC ladder.
Download Neural Modeling of Magnetic Tape Recorders The sound of magnetic recording media, such as open-reel and cassette tape recorders, is still sought after by today’s sound practitioners due to the imperfections embedded in the physics of the magnetic recording process. This paper proposes a method for digitally emulating this character using neural networks. The signal chain of the proposed system consists of three main components: the hysteretic nonlinearity and filtering jointly produced by the magnetic recording process as well as the record and playback amplifiers, the fluctuating delay originating from the tape transport, and the combined additive noise component from various electromagnetic origins. In our approach, the hysteretic nonlinear block is modeled using a recurrent neural network, while the delay trajectories and the noise component are generated using separate diffusion models, which employ U-net deep convolutional neural networks. According to the conducted objective evaluation, the proposed architecture faithfully captures the character of the magnetic tape recorder. The results of this study can be used to construct virtual replicas of vintage sound recording devices with applications in music production and audio antiquing tasks.
Download Feature design for the classification of audio effect units by input/output measurements Virtual analog modeling is an important field of digital audio signal processing. It allows to recreate the tonal characteristics of real-world sound sources or to impress the specific sound of a certain analog device upon a digital signal on a software basis. Automatic virtual analog modeling using black-box system identification based on input/output (I/O) measurements is an emerging approach, which can be greatly enhanced by specific pre-processing methods suggesting the best-fitting model to be optimized in the actual identification process. In this work, several features based on specific test signals are presented allowing to categorize instrument effect units into classes of effects, like distortion, compression, modulations and similar categories. The categorization of analog effect units is especially challenging due to the wide variety of these effects. For each device, I/O measurements are performed and a set of features is calculated to allow the classification. The features are computed for several effect units to evaluate their applicability using a basic classifier based on pattern matching.
Download Block Processing Strategies for Computationally Efficient Dynamic Range Controllers This paper presents several strategies for designing Dynamic Range Controllers when using a block-based processing scheme instead of sample-by-sample processing scheme. The processes of energy measurement, gain calculus, and time constant selection are executed only once per each new incoming block of samples. Then, a simple and continuous gain update is computed and applied sample-by-sample between continuous sample blocks to achieve good sound quality and performance. This approach allows reducing the computational cost needs while maintaining the flexibility and behavior of sample-by-sample processing solutions. Several implementation optimizations are also presented for reducing the computational cost and achieving a flexible and better sounding dynamic curve using configurable soft knees or gain tables. The proposed approach has been tested and implemented in a modern DSP, achieving satisfactory results with a considerable computational costs saving.
Download Evaluating Neural Networks Architectures for Spring Reverb Modelling Reverberation is a key element in spatial audio perception, historically achieved with the use of analogue devices, such as plate and spring reverb, and in the last decades with digital signal processing techniques that have allowed different approaches for Virtual Analogue Modelling (VAM). The electromechanical functioning of the spring reverb makes it a nonlinear system that is difficult to fully emulate in the digital domain with white-box modelling techniques. In this study, we compare five different neural network architectures, including convolutional and recurrent models, to assess their effectiveness in replicating the characteristics of this audio effect. The evaluation is conducted on two datasets at sampling rates of 16 kHz and 48 kHz. This paper specifically focuses on neural audio architectures that offer parametric control, aiming to advance the boundaries of current black-box modelling techniques in the domain of spring reverberation.
Download Antialiasing Piecewise Polynomial Waveshapers Memoryless waveshapers are commonly used in audio signal processing. In discrete time, they suffer from well-known aliasing artifacts. We present a method for applying antiderivative antialising (ADAA), which mitigates aliasing, to any waveshaping function that can be represented as a piecewise polynomial. Specifically, we treat the special case of a piecewise linear waveshaper. Furthermore, we introduce a method for for replacing the sharp corners and jump discontinuities in any piecewise linear waveshaper with smoothed polynomial approximations, whose derivatives match the adjacent line segments up to a specified order. This piecewise polynomial can again be antialiased as a special case of the general piecewise polynomial. Especially when combined with light oversampling, these techniques are effective at reducing aliasing and the proposed method for rounding corners in piecewise linear waveshapers can also create more “realistic” analog-style waveshapers than standard piecewise linear functions.
Download Unsupervised Estimation of Nonlinear Audio Effects: Comparing Diffusion-Based and Adversarial Approaches Accurately estimating nonlinear audio effects without access to
paired input-output signals remains a challenging problem. This
work studies unsupervised probabilistic approaches for solving this
task. We introduce a method, novel for this application, based
on diffusion generative models for blind system identification, enabling the estimation of unknown nonlinear effects using blackand gray-box models. This study compares this method with a
previously proposed adversarial approach, analyzing the performance of both methods under different parameterizations of the
effect operator and varying lengths of available effected recordings. Through experiments on guitar distortion effects, we show
that the diffusion-based approach provides more stable results and
is less sensitive to data availability, while the adversarial approach
is superior at estimating more pronounced distortion effects. Our
findings contribute to the robust unsupervised blind estimation of
audio effects, demonstrating the potential of diffusion models for
system identification in music technology.
Download Generalizing Root Variable Choice in Wave Digital Filters with Grouped Nonlinearities Previous grouped-nonlinearity formulations for Wave Digital Filter (WDF) modeling of nonlinear audio circuits assumed that nonlinear (NL) devices with memoryless voltage–current characteristics were modeled as voltage-controlled current sources (VCCSs). These formulations cannot accommodate nonlinear devices whose equations cannot be written as NL VCCSs, and they cannot accommodate circuits with cutsets composed entirely of current sources (including NL VCCSs). In this paper we generalize independent and dependent variable choice at the root of WDF trees to accommodate both these cases, and review two graph theorems for avoiding forbidden cutsets and loops in general.
Download Modelling of nonlinear state-space systems using a deep neural network In this paper we present a new method for the pseudo black-box modelling of general continuous-time state-space systems using a discrete-time state-space system with an embedded deep neural network. Examples are given of how this method can be applied to a number of common nonlinear electronic circuits used in music technology, namely two kinds of diode-based guitar distortion circuits and the lowpass filter of the Korg MS-20 synthesizer.