NAT:用于实时交互场景的神经声学传输
IEEE transactions on visualization and computer graphics
|October 6, 2025
概括
神经声学转移使用隐性神经表征来实时预测声音场,克服了动态环境中以前方法的局限性. 这种方法通过高效地建模复杂的声音行为来增强交互式应用程序.
科学领域:
- 声学 声学 在声学方面
- 计算机科学 计算机科学
- 信号处理 信号处理
背景情况:
- 传统的声学传输方法需要大量的预计算,并与动态的场景变化作斗争.
- 在实时中表示和染波动的声传输分布是具有挑战性的.
研究的目的:
- 介绍神经声学转移,一种用于实时预测动态演变的声音场的新方法.
- 为了在复杂,不断变化的声环境中实现高效,准确的声音行为建模.
主要方法:
- 利用隐性神经表示来编码声传递函数及其变化.
- 开发基于蒙特卡洛的边界元素方法 (BEM) 快速近似方法,以生成高质量的培训数据.
- 实施策略,以减轻在训练数据合成期间的奇点.
主要成果:
- 在30秒的音频中,实现了声场的实时预测,运行时间效率在毫秒内.
- 通过在各种声传输场景中的验证,证明了数值准确性和效率.
- 成功建模了复杂的声音辐射空间和动态环境相互作用.
结论:
- 神经声学转移有效地模拟动态环境中的声音行为,克服了先前方法的局限性.
- 该方法为虚拟现实和增强现实等交互应用提供了高效和准确的解决方案.
- 隐式神经表示与BEM近似相结合,为实时声学建模提供了强大的框架.
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