Differentiable Modal Synthesis for Physical Modeling of Planar String Sound and Motion Simulation
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866912096808075264 |
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| author | Lee, Jin Woo Park, Jaehyun Choi, Min Jun Lee, Kyogu |
| author_facet | Lee, Jin Woo Park, Jaehyun Choi, Min Jun Lee, Kyogu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_05516 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Differentiable Modal Synthesis for Physical Modeling of Planar String Sound and Motion Simulation Lee, Jin Woo Park, Jaehyun Choi, Min Jun Lee, Kyogu Audio and Speech Processing Artificial Intelligence Sound Signal Processing 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. |
| title | Differentiable Modal Synthesis for Physical Modeling of Planar String Sound and Motion Simulation |
| topic | Audio and Speech Processing Artificial Intelligence Sound Signal Processing |
| url | https://arxiv.org/abs/2407.05516 |