Download Modal Audio Effects: A Carillon Case Study Modal representations—decomposing the resonances of objects into their vibrational modes has historically been a powerful tool for studying and synthesizing the sounds of physical objects, but it also provides a flexible framework for abstract sound synthesis. In this paper, we demonstrate a variety of musically relevant ways to modify the model upon resynthesis employing a carillon model as a case study. Using a set of audio recordings of the sixty bells of the Robert and Ann Lurie Carillon recorded at the University of Michigan, we present a modal analysis of these recordings, in which we decompose the sound of each bell into a sum of decaying sinusoids. Each sinusoid is characterized by a modal frequency, exponential decay rate, and initial complex amplitude. This analysis yields insight into the timbre of each individual bell as well as the entire carillon as an ensemble. It also yields a powerful parametric synthesis model for reproducing bell sounds and bell-based audio effects.
Download Moog Ladder Filter Generalizations Based on State Variable Filters We propose a new style of continuous-time filter design composed
of a cascade of 2nd-order state variable filters (SVFs) and a global
feedback path. This family of filters is parameterized by the SVF
cutoff frequencies and resonances, as well as the global feedback
amount. For the case of two identical SVFs in cascade and a specific value of the SVF resonance, the proposed design reduces to
the well-known Moog ladder filter. For another resonance value,
it approximates the Octave CAT filter. The resonance parameter
can be used to create new filters as well. We study the pole loci
and transfer functions of the SVF building block and entire filter.
We focus in particular on the effect of the proposed parameterization on important aspects of the filter’s response, including the
passband gain and cutoff frequency error. We also present the first
in-depth study of the Octave CAT filter circuit.
Download Energy-Preserving Time-Varying Schroeder Allpass Filters In artificial reverb algorithms, gains are commonly varied over
time to break up temporal patterns, improving quality. We propose
a family of novel Schroeder-style allpass filters that are energypreserving under arbitrary, continuous changes of their gains over
time. All of them are canonic in delays, and some are also canonic
in multiplies. This yields several structures that are novel even in
the time-invariant case. Special cases for cascading and nesting
these structures with a reduced number of multipliers are shown as
well. The proposed structures should be useful in artificial reverb
applications and other time-varying audio effects based on allpass
filters, especially where allpass filters are embedded in feedback
loops and stability may be an issue.
Download An Equivalent Circuit Interpretation of Antiderivative Antialiasing The recently proposed antiderivative antialiasing (ADAA) technique for stateful systems involves two key features: 1) replacing a nonlinearity in a physical model or virtual analog simulation
with an antialiased nonlinear system involving antiderivatives of
the nonlinearity and time delays and 2) introducing a digital filter
in cascade with each original delay in the system. Both of these
features introduce the same delay, which is compensated by adjusting the sampling period. The result is a simulation with reduced
aliasing distortion. In this paper, we study ADAA using equivalent
circuits, answering the question: “Which electrical circuit, discretized using the bilinear transform, yields the ADAA system?”
This gives us a new way of looking at the stability of ADAA and
how introducing extra filtering distorts a system’s response. We
focus on the Wave Digital Filter (WDF) version of this technique.
Download Modeling and Extending the Rca Mark Ii Sound Effects Filter We have analyzed the Sound Effects Filter from the one-of-a-kind RCA Mark II sound synthesizer and modeled it as a Wave Digital Filter using the Faust language, to make this once exclusive device widely available. By studying the original schematics and measurements of the device, we discovered several circuit modifications. Building on these, we proposed a number of extensions to the circuit which increase its usefulness in music production.
Download Time-Varying Filter Stability and State Matrix Products We show a new sufficient criterion for time-varying digital filter stability: that the matrix norm of the product of state matrices over a certain finite number of time steps is bounded by 1. This extends Laroche’s Criterion 1, which only considered one time step, while hinting at extensions to two time steps. Further extending these results, we also show that there is no intrinsic requirement that filter coefficients be frozen over any time scale, and extend to any dimension a helpful theorem that allows us to avoid explicitly performing eigen- or singular value decompositions in studying the matrix norm. We give a number of case studies on filters known to be time-varying stable, that cannot be proven time-varying stable with the original criterion, where the new criterion succeeds.
Download Antiderivative Antialiasing for Recurrent Neural Networks Neural networks have become invaluable for general audio processing tasks, such as virtual analog modeling of nonlinear audio equipment.
For sequence modeling tasks in particular, recurrent neural networks (RNNs) have gained widespread adoption in recent years. Their general applicability and effectiveness
stems partly from their inherent nonlinearity, which makes them
prone to aliasing. Recent work has explored mitigating aliasing
by oversampling the network—an approach whose effectiveness is
directly linked with the incurred computational costs. This work
explores an alternative route by extending the antiderivative antialiasing technique to explicit, computable RNNs. Detailed applications to the Gated Recurrent Unit and Long Short-Term Memory cell are shown as case studies. The proposed technique is evaluated
on multiple pre-trained guitar amplifier models, assessing its impact on the amount of aliasing and model tonality. The method is
shown to reduce the models’ tendency to alias considerably across
all considered sample rates while only affecting their tonality moderately, without requiring high oversampling factors. The results
of this study can be used to improve sound quality in neural audio
processing tasks that employ a suitable class of RNNs. Additional
materials are provided in the accompanying webpage.