Paper Archive

In the context of Computer Music, physical modeling is usually dedicated to the modeling of sound sources or physical instruments. This paper presents an innovative use of physical modeling in order to model and synthesize complex auditory effects such as collective acoustic phenomena producing metabolic emergent auditory organizations. As a case study, we chose the ‘dune effect’, which in open nature leads both to visual and auditory effects. The article introduces two particle physical models, able to collaborate. The first is dedicated the synthesis of spatial (or visual) dynamics effects of moving sand dunes. The second i s dedicated to the rendering of acoustical dynamics of “sounding sands”. Altogether, they lead to a multisensorial simulation of sand in dune. Keywords: auditory effects, metabolic effects, emergence, sonification process, sounding sands,

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This article introduces a physics-based real-time model of the output chain of a vacuum-tube amplifier. This output chain consists of a single-ended triode power amplifier stage, output transformer, and a loudspeaker. The simulation algorithm uses wave digital filters in digitizing the physical electric, mechanic, and acoustic subsystems. New simulation models for the output transformer and loudspeaker are presented. The resulting real-time model of the output chain allows any of the physical parameters of the system to be adjusted during run-time.

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Physical-modelling based sound resynthesis is considered by estimating physical model parameters for a clarinet-like system. Having as a starting point the pressure and flow signals in the mouthpiece, a two-stage optimisation routine is employed, in order to estimate a set of physical model parameters that can be used to resynthesise the original sound. Tested on numerically generated signals, the presented inverse-modelling method can almost entirely resynthesise the input sound. For signals measured under real playing conditions, captured by three microphones embedded in the instrument bore, the pressure can be successfully reproduced, while uncertainties in the fluid dynamical behaviour reveal that further model refinement is needed to reproduce the flow in the mouthpiece.

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A model for physically based synthesis of collision sounds is proposed. Attention is focused on the non-linear contact force, for which both analytical and experimental results are presented. Numerical implementation of the model is discussed, with regard to accuracy and efficiency issues. As an application, a physically based audio effect is presented.

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Due to advances in computational power, physical modelling for sound synthesis has gained an increased popularity over the past decades. Although much work has been done to accurately simulate existing physical systems, much less work exists on the use of physical modelling simply for the sake of creating sonically interesting sounds. This work presents a mass-spring network, inspired by existing models of the physical string. Masses have 2 translational degrees of freedom (DoF), and the springs have an additional equilibrium separation term, which together result in highly nonlinear effects. The main aim of this work is to create sonically interesting sounds while retaining some of the natural qualities of the physical string, as opposed to accurately simulating it. Although the implementation exhibits chaotic behaviour for certain choices of parameters, the presented system can create sonically interesting timbres, including nonlinear pitch glides and ‘wobbles’.

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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.

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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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The use of signal models is one of the key factors enabling us to establish high quality signal transformation algorithms with intuitive high level control parameters. In the present article we will discuss signal models, and the signal transformation algorithms that are based on these models, in relation to the physical properties of the sound source and the properties of human sound perception. We will argue that the implementation of perceptually intuitive high quality signal transformation algorithms requires strong links between the signal models and the perceptually relevant physical properties of the sound source. We will present an overview over the history of 2 sound models that are used for sound transformation and will show how the past and future evolution of sound transformation algorithms is driven by our understanding of the physical world.

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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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