# A study on more realistic room simulation for far-field keyword spotting

arXiv: https://arxiv.org/abs/2006.02774 · HTML: https://mlsanity.com/paper/2006.02774/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2020.

## Built on by (most cited first, 4 of 4)

- [MB-RIRs: a Synthetic Room Impulse Response Dataset with Frequency-Dependent Absorption Coefficients](https://mlsanity.com/paper/2507.09750/) (2025)
- [Treble10: A high-quality dataset for far-field speech recognition, dereverberation, and enhancement](https://mlsanity.com/paper/2510.23141/) (2025)
- [Improving multichannel speech enhancement through accurate room-acoustic simulations](https://mlsanity.com/paper/2606.31552/) (2026)
- [Training DeepFilterNet with Accurate Room Acoustic Simulations Improves Single-Channel Speech Enhancement](https://mlsanity.com/paper/2608.20971/) (2026)

## Abstract (arXiv)

We investigate the impact of more realistic room simulation for training far-field keyword spotting systems without fine-tuning on in-domain data. To this end, we study the impact of incorporating the following factors in the room impulse response (RIR) generation: air absorption, surface- and frequency-dependent coefficients of real materials, and stochastic ray tracing. Through an ablation study, a wake word task is used to measure the impact of these factors in comparison with a ground-truth set of measured RIRs. On a hold-out set of re-recordings under clean and noisy far-field conditions, we demonstrate up to 35.8% relative improvement over the commonly-used (single absorption coefficient) image source method. Source code is made available in the Pyroomacoustics package, allowing others to incorporate these techniques in their work.