Realistic sound is essential in virtual environments, such as computer games and mixed reality. Efficient and accurate numerical methods for pre-calculating acoustics have been developed over the last decade; however, pre-calculating acoustics makes handling dynamic scenes with moving sources challenging, requiring intractable memory storage. A physics-informed neural network (PINN) method in 1D is presented, which learns a compact and efficient surrogate model with parameterized moving Gaussian sources and impedance boundaries, and satisfies a system of coupled equations. The model shows relative mean errors below 2\%/0.2 dB and proposes a first step in developing PINNs for realistic 3D scenes.
翻译:在虚拟环境中,例如计算机游戏和混合现实中,现实的声音是必不可少的。过去十年来,预先计算声音的高效和准确数字方法已经发展;然而,预先计算的声音使处理动态场景的动源具有挑战性,需要难以存储的记忆。介绍了1D的物理知情神经网络(PINN)方法,该方法学习了一个具有参数移动高斯源和阻力界限的紧凑和有效的替代模型,并满足了一个组合方程式系统。该模型显示的相对平均错误低于2 ⁇ /0.2 dB, 并提出了为现实的3D场景开发PINN的第一步。