Download Bio-Inspired Optimization of Parametric Onset Detectors Onset detectors are used to recognize the beginning of musical
events in audio signals. Manual parameter tuning for onset detectors is a time consuming task, while existing automated approaches often maximize only a single performance metric. These
automated approaches cannot be used to optimize detector algorithms for complex scenarios, such as real-time onset detection
where an optimization process must consider both detection accuracy and latency. For this reason, a flexible optimization algorithm
should account for more than one performance metric in a multiobjective manner. This paper presents a generalized procedure for
automated optimization of parametric onset detectors. Our procedure employs a bio-inspired evolutionary computation algorithm
to replace manual parameter tuning, followed by the computation
of the Pareto frontier for multi-objective optimization. The proposed approach was evaluated on all the onset detection methods
of the Aubio library, using a dataset of monophonic acoustic guitar
recordings. Results show that the proposed solution is effective in
reducing the human effort required in the optimization process: it
replaced more than two days of manual parameter tuning with 13
hours and 34 minutes of automated computation. Moreover, the
resulting performance was comparable to that obtained by manual
optimization.
Download Quality Diversity for Synthesizer Sound Matching It is difficult to adjust the parameters of a complex synthesizer to
create the desired sound. As such, sound matching, the estimation of synthesis parameters that can replicate a certain sound, is
a task that has often been researched, utilizing optimization methods such as genetic algorithm (GA). In this paper, we introduce a
novelty-based objective for GA-based sound matching. Our contribution is two-fold. First, we show that the novelty objective is
able to improve the quality of sound matching by maintaining phenotypic diversity in the population. Second, we introduce a quality diversity approach to the problem of sound matching, aiming
to find a diverse set of matching sounds. We show that the novelty objective is effective in producing high-performing solutions
that are diverse in terms of specified audio features. This approach
allows for a new way of discovering sounds and exploring the capabilities of a synthesizer.
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 Non-Iterative Schemes for the Simulation of Nonlinear Audio Circuits In this work, a number of numerical schemes are presented in the
context of virtual-analog simulation. The schemes are linearlyimplicit in character, and hence directly solvable without iterative
methods. Schemes of increasing order of accuracy are constructed,
and convergence and stability conditions are proven formally. The
schemes are able to handle stiff problems very efficiently, because
of their fast update, and can be run at higher sample rates to reduce
aliasing. The cases of the diode clipper and ring modulator are
investigated in detail, including several numerical examples.
Download Spherical Decomposition of Arbitrary Scattering Geometries for Virtual Acoustic Environments A method is proposed to encode the acoustic scattering of objects for virtual acoustic applications through a multiple-input and
multiple-output framework. The scattering is encoded as a matrix in the spherical harmonic domain, and can be re-used and
manipulated (rotated, scaled and translated) to synthesize various
sound scenes. The proposed method is applied and validated using
Boundary Element Method simulations which shows accurate results between references and synthesis. The method is compatible
with existing frameworks such as Ambisonics and image source
methods.
Download Graph-Based Audio Looping and Granulation In this paper we describe similarity graphs computed from timefrequency analysis as a guide for audio playback, with the aim
of extending the content of fixed recordings in creative applications. We explain the creation of the graph from the distance between spectral frames, as well as several features computed from
the graph, such as methods for onset detection, beat detection, and
cluster analysis. Several playback algorithms can be devised based
on conditional pruning of the graph using these methods. We describe examples for looping, granulation, and automatic montage.
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.