论文标题
FRA-RIR：图像源方法的快速随机近似
FRA-RIR: Fast Random Approximation of the Image-source Method
论文作者
论文摘要
现代语音处理系统的培训通常需要大量的模拟房间脉冲响应（RIR）数据，以使系统能够在现实世界中的回响环境中良好地概括。但是，模拟现实的RIR数据通常需要准确的物理建模，并且此类仿真过程的加速通常需要某些计算平台，例如图形处理单元（GPU）。在本文中，我们提出了FRA-RIR，这是广泛使用的图像源方法（ISM）的快速随机近似方法，以有效地生成逼真的RIR数据，而无需特定的计算设备。 FRA-RIR通过一系列随机近似来代替标准ISM中的物理模拟，从而显着加快了仿真过程，并可以在直接的数据生成管道中应用。实验表明，FRA-RIR不仅可以比标准计算平台上其他现有的基于ISM的RIR仿真工具快得多，而且还可以改善使用模拟RIR训练在现实世界RIR上评估的语音剥夺系统的性能。可以在线获得FRA-RIR的Python实现\ footNote {\ url {https://github.com/yluo42/fra-rir}}}。
The training of modern speech processing systems often requires a large amount of simulated room impulse response (RIR) data in order to allow the systems to generalize well in real-world, reverberant environments. However, simulating realistic RIR data typically requires accurate physical modeling, and the acceleration of such simulation process typically requires certain computational platforms such as a graphics processing unit (GPU). In this paper, we propose FRA-RIR, a fast random approximation method of the widely-used image-source method (ISM), to efficiently generate realistic RIR data without specific computational devices. FRA-RIR replaces the physical simulation in the standard ISM by a series of random approximations, which significantly speeds up the simulation process and enables its application in on-the-fly data generation pipelines. Experiments show that FRA-RIR can not only be significantly faster than other existing ISM-based RIR simulation tools on standard computational platforms, but also improves the performance of speech denoising systems evaluated on real-world RIR when trained with simulated RIR. A Python implementation of FRA-RIR is available online\footnote{\url{https://github.com/yluo42/FRA-RIR}}.
