ISMIR 2025 Tutorial
Differentiable Physical Modeling Sound Synthesis: Theory, Musical Application, and Programming

Recent years have witnessed growing interest in bridging traditional sound synthesis methods with emerging machine learning technologies. This tutorial is motivated by the convergence of two previously distinct trajectories in audio research: physics-based sound synthesis and data-driven neural approaches. This session highlights how differentiable physical modeling opens new avenues for musical sound synthesis by combining the interpretability and realism of physical simulation with the learning capacity of modern neural networks. The tutorial is structured into five segments: an overview of digital synthesis history and physical modeling, a detailed introduction to finite difference time domain (FDTD) methods across various instrument classes, a broad survey of neural architectures relevant to physical modeling, an in-depth look at differentiable modeling for parameter estimation using automatic differentiation, and a concluding session to synthesize key takeaways. Attendees will engage with theoretical material, practical demonstrations, and programming exercises, gaining hands-on experience in combining physics-based simulation with neural networks.

This tutorial is designed for researchers and engineers interested in advanced sound synthesis, particularly those working in musical acoustics, AI-based audio modeling, or digital instrument design. It will benefit individuals seeking to build physically plausible audio models or hybrid machine learning systems for realistic sound generation. All ISMIR members are warmly encouraged to attend—whether newcomers or seasoned researchers. The tutorial is designed to be approachable rather than overly technical, while still offering a deep understanding of how differentiable simulation can enhance synthesis fidelity, support neural network training, and advance hybrid sound modeling.