Differentiable Modal Synthesis for Physical Modeling of Planar String Sound and Motion Simulation

Abstract

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 mechanical 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.

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Cite

Text

Lee et al. "Differentiable Modal Synthesis for Physical Modeling of Planar String Sound and Motion Simulation." Neural Information Processing Systems, 2024. doi:10.52202/079017-0032

Markdown

[Lee et al. "Differentiable Modal Synthesis for Physical Modeling of Planar String Sound and Motion Simulation." Neural Information Processing Systems, 2024.](https://mlanthology.org/neurips/2024/lee2024neurips-differentiable/) doi:10.52202/079017-0032

BibTeX

@inproceedings{lee2024neurips-differentiable,
  title     = {{Differentiable Modal Synthesis for Physical Modeling of Planar String Sound and Motion Simulation}},
  author    = {Lee, Jin Woo and Park, Jaehyun and Choi, Min Jun and Lee, Kyogu},
  booktitle = {Neural Information Processing Systems},
  year      = {2024},
  doi       = {10.52202/079017-0032},
  url       = {https://mlanthology.org/neurips/2024/lee2024neurips-differentiable/}
}