Differentiable Modal Synthesis for Physical Modeling of Planar String Sound and Motion Simulation
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Abstract (arXiv)
While significant advancements have been made in music generation and differentiable sound synthesis within machine learning and computer audition, the simulation of instrument vibration guided by physical laws has been underexplored. To address this gap, we introduce a novel model for simulating the spatio-temporal motion of nonlinear strings, integrating modal synthesis and spectral modeling within a neural network framework. Our model leverages physical properties and fundamental frequencies as inputs, outputting string states across time and space that solve the partial differential equation characterizing the nonlinear string. Empirical evaluations demonstrate that the proposed architecture achieves superior accuracy in string motion simulation compared to existing baseline architectures. The code and demo are available online.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 23 of its 66 references that are in the map. First posted on arXiv 7 Jul 2024.
Builds on
23 of 66 references.
References · 23
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11 more
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Built on by
Citing papers per year: 2025: 2; 2026: 2.
4 papers.
- 2025Fast Differentiable Modal Simulation of Non-linear Strings, Membranes, and Platescited by 3
- 2025Learning Nonlinear Dynamics in Physical Modelling Synthesis using Neural Ordinary Differential Equationscited by 2
- 2026Stable Differentiable Modal Synthesis for Learning Nonlinear Dynamicscited by 0
- 2026Identifying the nonlinear string dynamics with port-Hamiltonian neural networkscited by 0
- 2026Identifying the nonlinear string dynamics with port-Hamiltonian neural networks12 May 2026
- 2026Stable Differentiable Modal Synthesis for Learning Nonlinear Dynamics15 Jan 2026
- 2025Learning Nonlinear Dynamics in Physical Modelling Synthesis using Neural Ordinary Differential Equations15 May 2025
- 2025Fast Differentiable Modal Simulation of Non-linear Strings, Membranes, and Plates9 May 2025