Paper Archive

In this paper we propose an efficient method to model the amount of bow hair in contact with the string in a physical model of a bowed string instrument.

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Real time sound synthesis in computer games using physical modeling is an area of great potential. To date, most sounds are prerecorded to match a certain event. Instead, by using a model to describe the sound producing event, a number of problems encountered when using pre-recorded sounds can be avoided. This paper deals with the application of physical modeling to the sound synthesis of rainfall. The implementation of a real-time simulation and a graphics interface allowing an interactive control of rainfall sound are discussed.

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This paper describes the design and control of a digital synthesis engine developed to imitate the sound of an acoustic wind machine, a historical theatre sound effect first designed in the nineteenth century. This work is part of an exploration of the potential of historical theatre sound effects as a resource for Sonic Interaction Design (SID). The synthesis engine is based on a physical model of friction and is programmed using the Sound Designer’s Toolkit (SDT) suite of physical modelling objects in Max/MSP. The program is controlled in real-time with a single stream of rotation data from a rotary encoder and Arduino, with complexity achieved through a mapping strategy that recreates the mechanical process at the heart of the acoustic wind machine’s sound production. The system is outlined, along with a discussion of the possible application of this approach to the modeling of other historical theatre sound effects.

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GENESIS is a sound synthesis and musical creation environment based on the mass-interaction CORDIS-ANIMA physical modelling formalism. It has got the noteworthy property that it allows to work both on sound itself and on musical composition in a single coherent environment. In this paper we present the first results of a study that is carried out with GENESIS on a particular type of models: self-sustained oscillating structures. By trying to build physical models of real instruments like bowed strings or woodwinds, our aim is to develop and analyse generic tools that can be used for the production of self-sustained oscillations on every mass-interaction network built with GENESIS. But, if the family of the self-sustained oscillating structures is very interesting to create rich timbres, it can also play a new and fundamental role at the level of the temporal macrostructure of the music (that of the gesture and the instrumental performance, as well as the composition). Indeed, it is possible, as we will propose in this paper, to use the relatively complex motion of a bowed macrostructure in a musical composition way, as a musical events generator.

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A variety of filters have been designed, synthesized and used in the history of electronic and computer music. All the approaches aimed to provide filters fulfilling several specifications such as frequency response, phase response, transient state characteristics like rise time and overshoot, realizably conditions concerning the technology used for the implementation and even economical considerations. One of the most important aspects concerning the filters dedicated to musical applications is the control structure they provide to the musician, who is in charge for the integration of the filtering operation in the compositional process and performance. Designing filters using the mass interaction scheme embedded in the CORDIS-ANIMA formalism (used for sound synthesis and composition by physical modelling) offers a different methodology in the control which is coherent with the philosophy of musical composition by ‘physical thinking’. This article introduces a technique to design filters using the CORDIS-ANIMA simulation language.

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Physical circuit models have an inherent ability to simulate the behaviour of user controls as exhibited by, for example, potentiometers. Working to accurately model the user interface of musical circuits, this work provides potentiometer ‘laws’ that fit to the underlying characteristics of linear and logarithmic potentiometers. A strategy of identifying these characteristics is presented, exclusively using input/output measurements and as such avoiding device disassembly. By breaking down the identification problem into one dimensional, search spaces characteristics are successfully identified. The proposed strategy is exemplified through a case study on the tone stack of the Big Muff Pi.

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This study investigates the use of an unsupervised, physicsinformed deep learning framework to model a one-degree-offreedom mass-spring system subjected to a nonlinear friction bow force and governed by a set of ordinary differential equations. Specifically, it examines the application of Physics-Informed Neural Networks (PINNs) and Physics-Informed Deep Operator Networks (PI-DeepONets). Our findings demonstrate that PINNs successfully address the problem across different bow force scenarios, while PI-DeepONets perform well under low bow forces but encounter difficulties at higher forces. Additionally, we analyze the Hessian eigenvalue density and visualize the loss landscape. Overall, the presence of large Hessian eigenvalues and sharp minima indicates highly ill-conditioned optimization. These results underscore the promise of physics-informed deep learning for nonlinear modelling in musical acoustics, while also revealing the limitations of relying solely on physics-based approaches to capture complex nonlinearities. We demonstrate that PI-DeepONets, with their ability to generalize across varying parameters, are well-suited for sound synthesis. Furthermore, we demonstrate that the limitations of PI-DeepONets under higher forces can be mitigated by integrating observation data within a hybrid supervised-unsupervised framework. This suggests that a hybrid supervised-unsupervised DeepONets framework could be a promising direction for future practical applications.

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The FAUST Synthesis ToolKit is a set of virtual musical instruments written in the FAUST programming language and based on waveguide algorithms and on modal synthesis. Most of them were inspired by instruments implemented in the Synthesis ToolKit (STK) and the program SynthBuilder. Our attention has partly been focused on the pedagogical aspect of the implemented objects. Indeed, we tried to make the FAUST code of each object as optimized and as expressive as possible. Some of the instruments in the FAUST-STK use nonlinear allpass filters to create interesting and new behaviors. Also, a few of them were modified in order to use gesture data to control the performance. A demonstration of this kind of use is done in the Pure Data program. Finally, the results of some performance tests of the generated C++ code are presented.

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In this paper, we present a revised model of the plectrum-string interaction and its interface with the digital waveguide for simulation of the harpsichord sound. We will first revisit the plectrum body model that we have proposed previously in [1] and then extend the model to incorporate the geometry of the plectrum tip. This permits us to model the dynamics of the string slipping off the plectrum more comprehensively, which provides more physically accurate excitation signals. Simulation results are presented and discussed.

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For physical modelling sound synthesis, many techniques are available; time-stepping methods (e.g., finite-difference time-domain (FDTD) methods) have an advantage of flexibility and generality in terms of the type of systems they can model. These methods do, however, lack the capability of easily handling smooth parameter changes while retaining optimal simulation quality and stability, something other techniques are better suited for. In this paper, we propose an efficient method to smoothly add and remove grid points from a FDTD simulation under sub-audio rate parameter variations. This allows for dynamic parameter changes in physical models of musical instruments. An instrument such as the trombone can now be modelled using FDTD methods, as well as physically impossible instruments where parameters such as e.g. material density or its geometry can be made time-varying. Results show that the method does not produce (visible) artifacts and stability analysis is ongoing.

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