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.
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.