Download Differentiable White-Box Virtual Analog Modeling Component-wise circuit modeling, also known as “white-box”
modeling, is a well established and much discussed technique in
virtual analog modeling. This approach is generally limited in accuracy by lack of access to the exact component values present in
a real example of the circuit. In this paper we show how this problem can be addressed by implementing the white-box model in a
differentiable form, and allowing approximate component values
to be learned from raw input–output audio measured from a real
device.
Download Practical Virtual Analog Modeling Using Möbius Transforms Möbius transforms provide for the definition of a family of onestep discretization methods offering a framework for alleviating
well-known limitations of common one-step methods, such as the
trapezoidal method, at no cost in model compactness or complexity. In this paper, we extend the existing theory around these methods. Here, we show how it can be applied to common frameworks
used to structure virtual analog models. Then, we propose practical strategies to tune the transform parameters for best simulation
results. Finally, we show how such strategies enable us to formulate much improved non-oversampled virtual analog models for
several historical audio circuits.
Download Amp-Space: A Large-Scale Dataset for Fine-Grained Timbre Transformation We release Amp-Space, a large-scale dataset of paired audio
samples: a source audio signal, and an output signal, the result of
a timbre transformation. The types of transformations we study
are from blackbox musical tools (amplifiers, stompboxes, studio
effects) traditionally used to shape the sound of guitar, bass, or
synthesizer sounds. For each sample of transformed audio, the
set of parameters used to create it are given. Samples are from
both real and simulated devices, the latter allowing for orders of
magnitude greater data than found in comparable datasets. We
demonstrate potential use cases of this data by (a) pre-training a
conditional WaveNet model on synthetic data and show that it reduces the number of samples necessary to digitally reproduce a
real musical device, and (b) training a variational autoencoder to
shape a continuous space of timbre transformations for creating
new sounds through interpolation.
Download Damped Chirp Mixture Estimation via Nonlinear Bayesian Regression Estimating mixtures of damped chirp sinusoids in noise is a
problem that affects audio analysis, coding, and synthesis applications. Phase-based non-stationary parameter estimators assume
that sinusoids can be resolved in the Fourier transform domain,
whereas high-resolution methods estimate superimposed components with accuracy close to the theoretical limits, but only for
sinusoids with constant frequencies. We present a new method
for estimating the parameters of superimposed damped chirps that
has an accuracy competitive with existing non-stationary estimators but also has a high-resolution like subspace techniques. After providing the analytical expression for a Gaussian-windowed
damped chirp signal’s Fourier transform, we propose an efficient
variational EM algorithm for nonlinear Bayesian regression that
jointly estimates the amplitudes, phases, frequencies, chirp rates,
and decay rates of multiple non-stationary components that may be
obfuscated under the same local maximum in the frequency spectrum. Quantitative results show that the new method not only has
an estimation accuracy that is close to the Cramér-Rao bound, but
also a high resolution that outperforms the state-of-the-art.
Download Sitrano: A Matlab App for Sines-Transients-Noise Decomposition of Audio Signals Decomposition of sounds into their sinusoidal, transient, and noise
components is an active research topic and a widely-used tool in
audio processing. Multiple solutions have been proposed in recent
years, using time–frequency representations to identify either horizontal and vertical structures or orientations and anisotropy in the
spectrogram of the sound. In this paper, we present SiTraNo: an
easy-to-use MATLAB application with a graphic user interface for
audio decomposition that enables visualization and access to the
sinusoidal, transient, and noise classes, individually. This application allows the user to choose between different well-known separation methods to analyze an input sound file, to instantaneously
control and remix its spectral components, and to visually check
the quality of the separation, before producing the desired output
file. The visualization of common artifacts, such as birdies and
dropouts, is demonstrated. This application promotes experimenting with the sound decomposition process by observing the effect
of variations for each spectral component on the original sound
and by comparing different methods against each other, evaluating
the separation quality both audibly and visually. SiTraNo and its
source code are available on a companion website and repository.
Download On the Estimation of Sinusoidal Parameters via Parabolic Interpolation of Scaled Magnitude Spectra Sinusoids are widely used to represent the oscillatory modes of
music and speech. The estimation of the sinusoidal parameters
directly affects the quality of the representation. A parabolic interpolation of the peaks of the log-magnitude spectrum is commonly
used to get a more accurate estimation of the frequencies and the
amplitudes of the sinusoids at a relatively low computational cost.
Recently, Werner and Germain proposed an improved sinusoidal estimator that performs parabolic interpolation of the peaks
of a power-scaled magnitude spectrum. For each analysis window type and size, a power-scaling factor p is pre-calculated via
a computationally demanding heuristic. Consequently, the powerscaling estimation method is currently constrained to a few tabulated power-scaling factors for pre-selected window sizes, limiting
its practical applications. In this article, we propose a method to
obtain the power-scaling factor p for any window size from the
tabulated values. Additionally, we investigate the impact of zeropadding on the estimation accuracy of the power-scaled sinusoidal
parameter estimator.
Download Optimal Integer Order Approximation of Fractional Order Filters Fractional order filters have been studied since a long time,
along with their applications to many areas of physics and engineering. In particular, several solutions have been proposed in
order to approximate their frequency response with that of an ordinary filter. In this paper, we tackle this problem with a new approach: we solve analytically a simplified version of the problem
and we find the optimal placement of poles and zeros, giving a
mathematical proof and an error estimate. This solution shows improved performance compared to the current state of the art and is
suitable for real-time parametric control.
Download Conformal Maps for the Discretization of Analog Filters Near the Nyquist Limit We propose a new analog filter discretization method that is useful
for discretizing systems with features near or above the Nyquist
limit. A conformal mapping approach is taken, and we introduce
the peaking conformal map and shelving conformal map. The proposed method provides a close match to the original analog frequency response below half the sampling rate and is parameterizable, order preserving, and agnostic to the original filter’s order
or type. The proposed method should have applications to discretizing filters that have time-varying parameters or need to be
implemented across many different sampling rates.
Download Simulating a Hexaphonic Pickup Using Parallel Comb Filters for Guitar Distortion This paper introduces hexaphonic distortion as a way of achieving
harmonically rich guitar distortion while minimizing intermodulation products regardless of playing style. The simulated hexaphonic distortion effect described in this paper attempts to reproduce the characteristics of hexaphonic distortion for use with ordinary electric guitars with mono pickups. The proposed approach
uses a parallel comb filter structure that separates a mono guitar
signal into its harmonic components. This simulates the six individual string signals obtained from a hexaphonic pickup. Each of
the signals are then individually distorted with oversampling used
to avoid aliasing artifacts. Starting with the baseline of the distorted mono signal, the simulated distortion produces fewer intermodulation products with a result approaching that of hexaphonic
distortion.
Download Interacting With Digital Audio Effects Through a Haptic Knob With Programmable Resistance Live music performances and music production often involve the
manipulation of several parameters during sound generation, processing, and mixing. In hardware layouts, those parameters are
usually controlled using knobs, sliders and buttons. When these
layouts are virtualized, the use of physical (e.g. MIDI) controllers
can make interaction easier and reduce the cognitive load associated to sound manipulation. The addition of haptic feedback can
further improve such interaction by facilitating the detection of the
nature (continuous / discrete) and value of a parameter. To this
end, we have realized an endless-knob controller prototype with
programmable resistance to rotation, able to render various haptic effects. Ten subjects assessed the effectiveness of the provided
haptic feedback in a target-matching task where either visual-only
or visual-haptic feedback was provided; the experiment reported
significantly lower errors in presence of haptic feedback. Finally,
the knob was configured as a multi-parametric controller for a
real-time audio effect software written in Python, simulating the
voltage-controlled filter aboard the EMS VCS3. The integration
of the sound algorithm and the haptic knob is discussed, together
with various haptic feedback effects in response to control actions.