Various ways to implement infinitely rising or falling spectral notches, also known as the barberpole phaser and flanging illusions, are described and studied. The first method is inspired by the Shepard-Risset illusion, and is based on a series of several cascaded notch filters moving in frequency one octave apart from each other. The second method, called a synchronized dual flanger, realizes the desired effect in an innovative and economic way using two cascaded time-varying comb filters and cross-fading between them. The third method is based on the use of single-sideband modulation, also known as frequency shifting. The proposed techniques effectively reproduce the illusion of endlessly moving spectral notches, particularly at slow modulation speeds and for input signals with a rich frequency spectrum. These effects can be programmed in real time and implemented as part of a digital audio processing system.

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The Serge Triple Waveshaper (TWS) is a synthesizer module designed in 1973 by Serge Tcherepnin, founder of Serge Modular Music Systems. It contains three identical waveshaping circuits that can be used to convert sawtooth waveforms into sine waves. However, its sonic capabilities extend well beyond this particular application. Each processing section in the Serge TWS is built around what is known as a Norton amplifier. These devices, unlike traditional operational amplifiers, operate on a current differencing principle and are featured in a handful of iconic musical circuits. This work provides an overview of Norton amplifiers within the context of virtual analog modeling and presents a digital model of the Serge TWS based on an analysis of the original circuit. Results obtained show the proposed model closely emulates the salient features of the original device and can be used to generate the complex waveforms that characterize “West Coast” synthesis.

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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.

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In this paper we present an approach to using traditional digital IIR filter structures inside deep-learning networks trained using backpropagation. We establish the link between such structures and recurrent neural networks. Three different differentiable IIR filter topologies are presented and compared against each other and an established baseline. Additionally, a simple Wiener-Hammerstein model using differentiable IIRs as its filtering component is presented and trained on a guitar signal played through a Boss DS-1 guitar pedal.

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