在现实的交互式3D场景中,使用深度神经运算符使用参数化源进行声音传播
Nikolas Borrel-Jensen1, Somdatta Goswami2, Allan P Engsig-Karup3
1Department of Electrical and Photonics Engineering, Acoustic Technology, Technical University of Denmark, Kongens Lyngby 2800, Denmark.
概括
我们开发了一种使用深度操作员网络进行3D声学模拟的快速方法. 这使得在虚拟环境中实时进行声音传播预测,克服了传统方法的计算限制.
科学领域:
- 声学 声学 在声学上
- 计算物理学的计算物理.
- 机器学习是机器学习.
背景情况:
- 准确的声学模拟对于虚拟/增强现实,游戏音频和空间计算至关重要.
- 解决波方程的传统数值方法是计算密集的,特别是对参数源位置.
- 这限制了在动态虚拟环境中详细的声学模拟的实际应用.
研究的目的:
- 开发一种计算效率高的方法,用于预测具有参数源位置的3D虚拟房间中的声音传播.
- 为了克服波浪现象的传统数值离散方法的局限性.
- 为了实现虚拟现实和游戏音频等应用程序的实时声学模拟.
主要方法:
- 使用深度运算子网络来近似线性波方程运算子.
- 开发一个紧的代孕模型,以避免预先计算和存储冲动反应.
- 将该方法应用于各种复杂的3D声学场景几何形状.
主要成果:
- 实现了用于声音传播预测的毫秒级计算时间.
- 在不同复杂的场景中与参考解决方案达成良好一致.
- 报告的根平均平方误差在0.02和0.10Pa之间.
结论:
- 深度运营商网络为具有参数源位置的3D声学模拟提供了计算效率高的解决方案.
- 提出的方法代表了范式的转变,使得精确的波场预测在现实的领域.
- 这一进步在虚拟和增强现实应用中促进了更具身临其境和现实的音频体验.
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