Download Time-Variant Gray-Box Modeling of a Phaser Pedal A method to measure the response of a linear time-variant (LTV) audio system is presented. The proposed method uses a series of short chirps generated as the impulse response of several cascaded allpass filters. This test signal can measure the characteristics of an LTV system as a function of time. Results obtained from testing of this method on a guitar phaser pedal are presented. A proof of concept gray-box model of the measured system is produced based on partial knowledge about the internal structure of the pedal and on the spectral analysis of the measured responses. The temporal behavior of the digital model is shown to be very similar to that of the measured device. This demonstrates that it is possible to measure LTV analog audio systems and produce approximate virtual analog models based on these results.
Download Low Frequency Group Delay Equalization of Vented Boxes using Digital Correction Filters In this paper methods to determine the group delay of vented boxes and techniques for the design of filters for group delay equalization are presented. First the transfer function and the related group delay are explained. Then it is shown how the group delay can be computed or approximated for a certain alignment of the box. Furthermore it is shown how to derive the required parameters of the transfer function from a simple electrical measurement of the box, which allows the determination of the group delay without knowledge of the box design parameters. Two strategies for the design and implementation of digital correction filters are shown where one approach allows for a real-time adjustability of the delay. Finally, the performance with a real speaker is evaluated.
Download Doppler Simulation and the Leslie An efficient algorithm for simulating the Doppler effect using interpolating and de-interpolating delay lines is described. The Doppler simulator is used to simulate a rotating horn to achieve the Leslie effect. Measurements of a horn from a real Leslie are used to calibrate angle-dependent digital filters which simulate the changing, angle-dependent, frequency response of the rotating horn.
Download Physical Model Parameter Optimisation for Calibrated Emulation of the Dallas Rangemaster Treble Booster Guitar Pedal In this work we explore optimising parameters of a physical circuit model relative to input/output measurements, using the Dallas Rangemaster Treble Booster as a case study. A hybrid metaheuristic/gradient descent algorithm is implemented, where the initial parameter sets for the optimisation are informed by nominal values from schematics and datasheets. Sensitivity analysis is used to screen parameters, which informs a study of the optimisation algorithm against model complexity by fixing parameters. The results of the optimisation show a significant increase in the accuracy of model behaviour, but also highlight several key issues regarding the recovery of parameters.
Download Rounding Corners with BLAMP The use of the bandlimited ramp (BLAMP) function as an antialiasing tool for audio signals with sharp corners is presented. Discontinuities in the waveform of a signal or its derivatives require infinite bandwidth and are major sources of aliasing in the digital domain. A polynomial correction function is modeled after the ideal BLAMP function. This correction function can be used to treat aliasing caused by sharp edges or corners which translate into discontinuities in the first derivative of a signal. Four examples of cases where these discontinuities appear are discussed: synthesis of triangular waveforms, hard clipping, and half-wave and fullwave rectification. Results obtained show that the BLAMP function is a more efficient tool for alias reduction than oversampling. The polynomial BLAMP can reduce the level of aliasing components by up to 50 dB and improve the overall signal-to-noise ratio by about 20 dB. The proposed method can be incorporated into virtual analog models of musical systems.
Download New techniques and Effects in Model-based Sound Synthesis Physical modeling and model-based sound synthesis have recently been among the most active topics of computer music and audio research. In the modeling approach one typically tries to simulate and duplicate the most prominent sound generation properties of the acoustic musical instrument under study. If desired, the models developed may then be modified in order to create sounds that are not common or even possible from physically realizable instruments. In addition to physically related principles it is possible to combine physical models with other synthesis and signal processing methods to realize hybrid modeling techniques.
This article is written as an overview of some recent results in model-based sound synthesis and related signal processing techniques. The focus is on modeling and synthesizing plucked string sounds, although the techniques may find much more widespread application. First, as a background, an advanced linear model of the acoustic guitar is discussed along with model control principles. Then the methodology to include inherent nonlinearities and time-varying features is introduced. Examples of string instrument nonlinearities are studied in the context of two specific instruments, the kantele and the tanbur, which exhibit interesting nonlinear effects.
Download Biquad Coefficients Optimization via Kolmogorov-Arnold Networks Conventional Deep Learning (DL) approaches to Infinite Impulse
Response (IIR) filter coefficients estimation from arbitrary frequency response are quite limited. They often suffer from inefficiencies such as tight training requirements, high complexity, and
limited accuracy. As an alternative, in this paper, we explore the
use of Kolmogorov-Arnold Networks (KANs) to predict the IIR
filter—specifically biquad coefficients—effectively. By leveraging the high interpretability and accuracy of KANs, we achieve
smooth coefficients’ optimization. Furthermore, by constraining
the search space and exploring different loss functions, we demonstrate improved performance in speed and accuracy. Our approach
is evaluated against other existing differentiable IIR filter solutions. The results show significant advantages of KANs over existing methods, offering steadier convergences and more accurate
results. This offers new possibilities for integrating digital infinite
impulse response (IIR) filters into deep-learning frameworks.
Download Automatic Segmentation of the Temporal Evolution of Isolated Acoustic Musical Instruments Sounds Using Spectro-Temporal Cues The automatic segmentation of isolated musical instrument sounds according to the temporal evolution is not a trivial task. It requires a model capable of capturing regions such as the attack, decay, sustain and release accurately for many types of instruments with different modes of excitation. The traditional ADSR amplitude envelope model does not apply universally to acoustic musical instrument sounds with different excitation methods because it uses strictly amplitude information and supposes all sounds manifest the same temporal evolution. We present an automatic segmentation technique based on a more realistic model of the temporal evolution of many types of acoustic musical instruments that incorporates both temporal and spectrotemporal cues. The method allows a robust and more perceptually relevant automatic segmentation of the isolated sounds of many musical instruments that fit the model.
Download Physical Modeling Using Recurrent Neural Networks with Fast Convolutional Layers Discrete-time modeling of acoustic, mechanical and electrical systems is a prominent topic in the musical signal processing literature. Such models are mostly derived by discretizing a mathematical model, given in terms of ordinary or partial differential equations, using established techniques. Recent work has applied the techniques of machine-learning to construct such models automatically from data for the case of systems which have lumped states described by scalar values, such as electrical circuits. In this work, we examine how similar techniques are able to construct models of systems which have spatially distributed rather than lumped states. We describe several novel recurrent neural network structures, and show how they can be thought of as an extension of modal techniques. As a proof of concept, we generate synthetic data for three physical systems and show that the proposed network structures can be trained with this data to reproduce the behavior of these systems.