Differentiable Modal Synthesis for Physical Modeling of Planar String Sound and Motion Simulation
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Music GenerationSimilar Papers 제목 키워드 기반
Gaussian-Augmented Physics Simulation and System Identification with Complex Colliders
System identification involving the geometry, appearance, and physical properties from video observations is a challenging task with applications in robotics and graphics. Recent approaches have relied on fully different…
Novel View SynthesisRigid-Body Sound Synthesis with Differentiable Modal Resonators
Physical models of rigid bodies are used for sound synthesis in applications from virtual environments to music production. Traditional methods such as modal synthesis often rely on computationally expensive numerical so…
Fast Differentiable Modal Simulation of Non-linear Strings, Membranes, and Plates
Modal methods for simulating vibrations of strings, membranes, and plates are widely used in acoustics and physically informed audio synthesis. However, traditional implementations, particularly for non-linear models lik…
Audio SynthesisCPUGPUNeurMiPs: Neural Mixture of Planar Experts for View Synthesis
We present Neural Mixtures of Planar Experts (NeurMiPs), a novel planar-based scene representation for modeling geometry and appearance. NeurMiPs leverages a collection of local planar experts in 3D space as the scene re…
Novel View SynthesisPhysics-Informed Neural Engine Sound Modeling with Differentiable Pulse-Train Synthesis
Engine sounds originate from sequential exhaust pressure pulses rather than sustained harmonic oscillations. While neural synthesis methods typically aim to approximate the resulting spectral characteristics, we propose …