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.

翻译：尽管机器学习和计算机听觉领域在音乐生成与可微分声音合成方面取得了显著进展，但遵循物理定律的乐器振动模拟研究仍显不足。为填补这一空白，我们提出了一种模拟非线性弦乐器时空运动的新型模型，该模型将模态合成与谱建模技术整合于神经网络框架中。本模型以物理属性与基频作为输入，通过求解表征非线性弦振动的偏微分方程，输出弦乐器在时空维度上的状态。实证评估表明，相较于现有基线架构，所提出的模型在弦乐器运动模拟方面实现了更高的精度。相关代码与演示已在线发布。