# FRA-RIR: Fast Random Approximation of the Image-source Method

arXiv: https://arxiv.org/abs/2208.04101 · HTML: https://mlsanity.com/paper/2208.04101/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2022.

## Built on by (most cited first, 5 of 5)

- [Towards Improved Room Impulse Response Estimation for Speech Recognition](https://mlsanity.com/paper/2211.04473/) (2022)
- [SonicSim: A customizable simulation platform for speech processing in moving sound source scenarios](https://mlsanity.com/paper/2410.01481/) (2024)
- [How much to Dereverberate? Low-Latency Single-Channel Speech Enhancement in Distant Microphone Scenarios](https://mlsanity.com/paper/2505.01338/) (2025)
- [Gaussian Process Regression of Steering Vectors With Physics-Aware Deep Composite Kernels for Augmented Listening](https://mlsanity.com/paper/2509.02571/) (2025)
- [PathRIR: Physics-Guided Acoustic Path Selection and Late-Tail Compensation for Fast Room Impulse Response Simulation](https://mlsanity.com/paper/2607.23293/) (2026)

## Abstract (arXiv)

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 onlinefootnoteurlhttps://github.com/yluo42/FRA-RIR.