Download Antiderivative Antialiasing for Stateful Systems Nonlinear systems, like e.g. guitar distortion effects, play an important role in musical signal processing. One major problem encountered in digital nonlinear systems is aliasing distortion. Consequently, various aliasing reduction methods have been proposed in the literature. One of these is based on using the antiderivative of the nonlinearity and has proven effective, but is limited to memoryless systems. In this work, it is extended to a class of stateful systems which includes but is not limited to systems with a single one-port nonlinearity. Two examples from the realm of virtual analog modeling show its applicability to and effectiveness for commonly encountered guitar distortion effect circuits.
Download Taming the Red Llama—Modeling a CMOS-Based Overdrive Circuit 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.
Download A Virtual Analog Model of the Edp Wasp VCF In this paper we present a virtual analog model of the voltagecontrolled filter used in the EDP Wasp synthesizer. This circuit is an interesting case study for virtual analog modeling due to its characteristic nonlinear and highly dynamic behavior which can be attributed to its unusual design. The Wasp filter consists of a state variable filter topology implemented using operational transconductance amplifiers (OTAs) as the cutoff-control elements and CMOS inverters in lieu of operational amplifiers, all powered by a unipolar power supply. In order to accurately model the behavior of the circuit we propose extended models for its nonlinear components, focusing particularly on the OTAs. The proposed component models are used inside a white-box circuit modeling framework to create a digital simulation of the filter which retains the interesting characteristics of the original device.
Download Combined Derivative/Antiderivative Antialiasing Nonlinear systems play an important role in musical signal processing, but their digital implementation suffers from the occurrence of aliasing distortion. Consequently, various aliasing reduction methods have been proposed in the literature. In this work, a novel approach is examined that uses samples of a signal’s derivative in addition to the signal’s samples themselves. This allows some aliasing reduction, but is usually insufficient on its own. However, it can elegantly and fruitfully be combined with antiderivative antialiasing to obtain an effective method. Unfortunately, it still compares unfavorably to oversampled antiderivative antialiasing. It may therefore be regarded as a negative result, but it may hopefully still form a basis for further developments.
Download Antialiased State Trajectory Neural Networks for Virtual Analog Modeling In recent years, virtual analog modeling with neural networks experienced an increase in interest and popularity. Many different modeling approaches have been developed and successfully applied. In this paper we do not propose a novel model architecture, but rather address the problem of aliasing distortion introduced from nonlinearities of the modeled analog circuit. In particular, we propose to apply the general idea of antiderivative antialiasing to a state-trajectory network (STN). Applying antiderivative antialiasing to a stateful system in general leads to an integral of a multivariate function that can only be solved numerically, which is too costly for real-time application. However, an adapted STN can be trained to approximate the solution while being computationally efficient. It is shown that this approach can decrease aliasing distortion in the audioband significantly while only moderately oversampling the network in training and inference.