Download Digital Grey Box Model of the Uni-Vibe Effects Pedal This paper presents a digital grey box model of a late 1960s era Shin-ei Uni-Vibe(r) 1 analog effects foot pedal. As an early phase shifter, it achieved wide success in popular music as a unique musical effect, noteworthy for its pulsating and throbbing modulation sounds. The Uni-Vibe is an early series all-pass phaser effect, where each first-order section is a discrete component phase splitter (no operational amplifiers). The dynamic sweeping movement of the effect arises from a single LFO-driven incandescent lamp opto-coupled to the light dependent resistors (LDRs) of each stage. The proposed method combines digital circuit models with measured LDR characteristics for the four phase shift stages of an original Uni-Vibe unit, resulting in an efficient emulation that preserves the character of the Uni-Vibe. In modeling this iconic effect, we also aim to offer some historical and technical insight into the exact nature of its unique sound.
Download A general-purpose deep learning approach to model time-varying audio effects Audio processors whose parameters are modified periodically over time are often referred as time-varying or modulation based audio effects. Most existing methods for modeling these type of effect units are often optimized to a very specific circuit and cannot be efficiently generalized to other time-varying effects. Based on convolutional and recurrent neural networks, we propose a deep learning architecture for generic black-box modeling of audio processors with long-term memory. We explore the capabilities of deep neural networks to learn such long temporal dependencies and we show the network modeling various linear and nonlinear, time-varying and time-invariant audio effects. In order to measure the performance of the model, we propose an objective metric based on the psychoacoustics of modulation frequency perception. We also analyze what the model is actually learning and how the given task is accomplished.
Download Improved Carillon Synthesis An improved and expanded method for carillon bell synthesis is proposed. Measurements of a carillon bell and its clapper were made to serve as the basis for an efficient synthesis framework. Mode frequencies, damping, and amplitudes are used to form a modal model fit to measurements. A parameterized clapper interaction model is proposed to drive the bell model, reproducing variation of timbre as the bell is played in different dynamic ranges. Reverberation of the belfry was measured from several listener perspectives and an efficient modal reverberation architecture is shown to model the sound of the bell from locations inside and outside the belfry.
Download Sound texture synthesis using Convolutional Neural Networks The following article introduces a new parametric synthesis algorithm for sound textures inspired by existing methods used for visual textures. Using a 2D Convolutional Neural Network (CNN), a sound signal is modified until the temporal cross-correlations of the feature maps of its log-spectrogram resemble those of a target texture. We show that the resulting synthesized sound signal is both different from the original and of high quality, while being able to reproduce singular events appearing in the original. This process is performed in the time domain, discarding the harmful phase recovery step which usually concludes synthesis performed in the time-frequency domain. It is also straightforward and flexible, as it does not require any fine tuning between several losses when synthesizing diverse sound textures. Synthesized spectrograms and sound signals are showcased, and a way of extending the synthesis in order to produce a sound of any length is also presented. We also discuss the choice of CNN, border effects in our synthesized signals and possible ways of modifying the algorithm in order to improve its current long computation time.
Download On the Impact of Ground Sound Rigid-body impact sound synthesis methods often omit the ground sound. In this paper we analyze an idealized ground-sound model based on an elastodynamic halfspace, and use it to identify scenarios wherein ground sound is perceptually relevant versus when it is masked by the impacting object’s modal sound or transient acceleration noise. Our analytical model gives a smooth, closed-form expression for ground surface acceleration, which we can then use in the Rayleigh integral or in an “acoustic shader” for a finite-difference time-domain wave simulation. We find that when modal sound is inaudible, ground sound is audible in scenarios where a dense object impacts a soft ground and scenarios where the impact point has a low elevation angle to the listening point.
Download Exploring the Sound of Chaotic Oscillators via Parameter Spaces Chaotic oscillators are exciting sources for sound production due to their simplicity in implementation combined with their rich sonic output. However, the richness comes with difficulty of control, which is paramount to both their detailed understanding and in live musical performance. In this paper, we propose perceptually motivated parameter planes as a framework for studying the behavior of chaotic oscillators for musical use. Motivated by analysis via winding numbers, we extend traditional study of chaotic oscillators by using local features that are perceptually inspired. We illustrate the framework on the example of variations of the circle map. However, the framework is applicable for a wide range of sound synthesis algorithms with nontrivial parametric mappings.
Download Fast Approximation of the Lambert W Function for Virtual Analog Modelling When modelling circuits one has often to deal with equations containing both a linear and an exponential part. If only a single exponential term is present or predominant, exact or approximate closed-form solutions can be found in terms of the Lambert W function. In this paper, we propose reformulating such expressions in terms of the Wright Omega function when specific conditions are met that are customary in practical cases of interest. This eliminates the need to compute an exponential term at audio rate. Moreover, we propose simple and real-time suitable approximations of the Omega function. We apply our approach to a static and a dynamic nonlinear system, obtaining digital models that have high accuracy, low computational cost, and are stable in all conditions, making the proposed method suitable for virtual analog modelling of circuits containing semiconductor devices.
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 Towards Inverse Virtual Analog Modeling Several digital signal processing approaches, generally referred to as Virtual Analog (VA) modeling, are currently under development for the software emulation of analog audio circuitry. The main purpose of VA modeling is to faithfully reproduce the behavior of real-world audio gear, e.g., distortion effects, synthesizers or amplifiers, using efficient algorithms. In this paper, however, we provide a preliminary discussion about how VA modeling can be exploited to infer the input signal of an analog audio system, given the output signal and the parameters of the circuit. In particular, we show how an inversion theorem known in circuit theory, and based on nullors, can be used for this purpose. As recent advances in Wave Digital Filter (WDF) theory allow us to implement circuits with nullors in a systematic fashion, WDFs prove to be useful tools for inverse VA modeling. WDF realizations of a nonlinear audio system and its inverse are presented as an example of application.
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.