Paper Archive

In this paper, a complete simulation of a trombone using finitedifference time-domain (FDTD) methods is proposed. In particular, we propose the use of a novel method to dynamically vary the number of grid points associated to the FDTD method, to simulate the fact that the physical dimension of the trombone’s resonator dynamically varies over time. We describe the different elements of the model and present the results of a real-time simulation.

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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.

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A new estimation technique is proposed which computes the control parameters of a physical model of a trumpet in order to simulate a recording of a real instrument. First, the physical constraints of the instrument and the prior knowledge about how a player controls a trumpet are described. This is taken into account during the design of the data set and guarantees that these constraints are respected. Then, an estimation procedure minimizes two perceptual similarity criteria in function of the control parameters. The first criterium expresses the difference of the spectral envelopes and the second one the difference in fundamental frequency. An optimization technique is proposed that yields an optimal solution for the fundamental frequency, and a conditional suboptimal solution for the spectral envelope. A robust implementation of the technique was developed for which it is shown that the estimated parameters are unique and that the optimization does not suffer from local minima.

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Physical modeling of string vibrations strongly depends on the conditions at the system boundaries. The more complex the boundary conditions are the more complex is the process of physical modeling. Based on prior works, this contribution derives a general concept for the incorporation of complex boundary conditions into a transfer function model designed with simple boundary conditions. The concept is related to control theory and separates the treatment of the boundary conditions from the design of the string model.

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Physical modelling of musical instruments is one possible approach to digital sound synthesis techniques. By the term physical modelling, we refer to the simulation of sound production mechanism of a musical instrument, which is modelled with reference to the physics using wave-guides. One of the fundamental parameters of such a physical model is the pitch, and so pitch period estimation is one of the first tasks of any analysis of such a model. In this paper, an algorithm based on the Dyadic Wavelet Transform has been investigated for pitch detection of musical signals. The wavelet transform is simply the convolution of a signal f(t) with a dialated and translated version of a single function called the mother wavelet that has to satisfy certain requirements. There are a wide variety of possible wavelets, but not all are appropriate for pitch detection. The performance of both linear phase wavelets (Haar, Morlet, and the spline wavelet) and minimum phase wavelets (Daubechies’ wavelets) have been investigated. The algorithm proposed here has proved to be simple, accurate, and robust to noise; it also has the potential of acceptable speed. A comparative study between this algorithm and the well-known autocorrelation function is also given. Finally, illustrative examples of different real guitar tones and other sound signals are given using the proposed algorithm. KEYWORDS Physical modeling – wavelet transform – pitch – autocorrelation function.

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Modal methods for simulating vibrations of strings, membranes, and plates are widely used in acoustics and physically informed audio synthesis. However, traditional implementations, particularly for non-linear models like the von Kármán plate, are computationally demanding and lack differentiability, limiting inverse modelling and real-time applications. We introduce a fast, differentiable, GPU-accelerated modal framework built with the JAX library, providing efficient simulations and enabling gradientbased inverse modelling. Benchmarks show that our approach significantly outperforms CPU and GPU-based implementations, particularly for simulations with many modes. Inverse modelling experiments demonstrate that our approach can recover physical parameters, including tension, stiffness, and geometry, from both synthetic and experimental data. Although fitting physical parameters is more sensitive to initialisation compared to methods that fit abstract spectral parameters, it provides greater interpretability and more compact parameterisation. The code is released as open source to support future research and applications in differentiable physical modelling and sound synthesis.

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This work addresses sound modeling using a combination of physical and signal models with a particular emphasis on the flute sound. For that purpose, analysis methods adapted to the nonstationary nature of sounds are developed. Further on parameters characterizing the sound from a perceptive and a physical point of view are extracted. The synthesis process is then designed to reproduce a perceptive effect and to simulate the physical behavior of the sound generating system. The correspondence between analysis and synthesis parameters is crucial and can be achieved using both mathematical and perceptive criteria. Real-time control of such models makes it possible to use specially designed interfaces mirroring already existing sound generators like traditional musical instruments.

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Current physics-based synthesis techniques tend to synthesize the interaction between different functional elements of a sound generator by treating it as a single system. However, when dealing with the physical modeling of complex sound generators this choice raises questions about the resulting flexibility of the adopted synthesis strategy. One way to overcome this problem is to approach it by individually synthesizing and discretizing the objects that contribute to the generation of sounds. In this paper we address the problem of how to automatize the process of physically modeling the interaction between objects, and how to make it dynamical. We will show that this can be done through the automatic definition and implementation of a topology model that adapts to the contact and proximity conditions between the considered objects.

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Physical modeling sound synthesis has achieved remarkable success in terms of its fidelity to reality. In many cases, since modeling of the physical system is performed on the sounding objects that already exist in the real world, observation precedes the model itself. Departing from this convention, this paper aims to physically model the acoustic characteristics of objects that do not necessarily exist in reality. Specifically, we study wave propagation on compact two-dimensional (2D) manifolds that are non-orientable surfaces, such as the Klein bottle that cannot be embedded in three-dimensional Euclidean space without self-intersection. We derive closed-form expressions for the eigenfrequencies and mode shapes of non-orientable 2D topologies and study their acoustic characteristics. The modal structures are verified through comparison with finite-difference time-domain simulations. The results demonstrate how the topological character formed by the boundaries influences the acoustic resonances, and how the quotient-space framework provides a practical route to reverb synthesis on geometries with no physical counterpart.

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This paper proposes a new macroscopic physical model of ferromagnetic coils used in audio circuits. To account for realistic saturation and hysteretic phenomena, this model combines statistical physics results, measurement-driven refinements and portHamiltonian formulations that guarantee passivity, thermodynamic consistency and composability according to both electric and thermal ports. As an illustration, the model is used to simulate a passive high-pass filter. Different types of audio inputs are considered and simulations are compared to measurements.

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