Article Dans Une Revue Frontiers in Signal Processing Année : 2025

Editorial: Sound synthesis through physical modeling

Maarten van Walstijn
  • Fonction : Auteur
Michele Ducceschi

Résumé

Physical modeling synthesis aims to simulate sound by solving the equations governing an instrument or acoustic system’s behaviour. This paradigm stands apart from signal- based methods by directly encapsulating the cause–and–effect relationships of sound production. Its appeal lies in the fidelity of transients, the mapping from parameters to sound, and the capacity to expose how instruments work. Over the past 50 years, models have grown from simple linear caricatures to treatments of strongly nonlinear, coupled systems, frictional contacts, nonlinear audio effects, nonlinear air flows, and more. That realism comes at the cost of greater numerical hurdles and, consequently, the need for schemes that remain stable and efficient. More recently, new tools have entered the picture. Machine learning is being used to tune or hybridise physical models, yielding differentiable systems that can learn while staying tied to physics. Energy-based formalisms such as port- Hamiltonian models provide a systematic way to enforce passivity in power-exchanging systems. At the same time, hardware–GPUs, FPGAs, modern CPUs–has opened up real- time simulation at scales that were out of reach only a decade ago. This Research Topic sits in that landscape. The contributions span multi-physics and nonlinear modelling, energy- consistent algorithms, machine learning assistance, and hardware acceleration, all aimed at sharpening accuracy and stability while keeping models playable in real-time. What follows is a tour of the six papers and of how they move the field forward.

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Dates et versions

hal-05378239 , version 1 (22-11-2025)

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Thomas Hélie, Maarten van Walstijn, Michele Ducceschi. Editorial: Sound synthesis through physical modeling. Frontiers in Signal Processing, 2025, 5, ⟨10.3389/frsip.2025.1715792⟩. ⟨hal-05378239⟩
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