  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}}.
