# Room-acoustic simulations as an alternative to measurements for audio-algorithm evaluation

arXiv: https://arxiv.org/abs/2509.05175 · HTML: https://mlsanity.com/paper/2509.05175/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 47 references that are in the map.
- First posted on arXiv 5 Sep 2025.

## Builds on (most cited first, 7 of 7)

- [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018)
- [Speech Enhancement and Dereverberation with Diffusion-based Generative Models](https://mlsanity.com/paper/2208.05830/) (2022)
- [Pyroomacoustics: A Python package for audio room simulations and array processing algorithms](https://mlsanity.com/paper/1710.04196/) (2017)
- [MeshRIR: A Dataset of Room Impulse Responses on Meshed Grid Points For Evaluating Sound Field Analysis and Synthesis Methods](https://mlsanity.com/paper/2106.10801/) (2021)
- [Generative Data Augmentation Challenge: Synthesis of Room Acoustics for Speaker Distance Estimation](https://mlsanity.com/paper/2501.13250/) (2025)
- [Speaker Distance Estimation in Enclosures from Single-Channel Audio](https://mlsanity.com/paper/2403.17514/) (2024)
- [MIRaGe: Multichannel Database Of Room Impulse Responses Measured On High-Resolution Cube-Shaped Grid In Multiple Acoustic Conditions](https://mlsanity.com/paper/1907.12421/) (2019)

## Built on by (most cited first, 3 of 3)

- [Room Impulse Response Completion Using Signal-Prediction Diffusion Models Conditioned on Simulated Early Reflections](https://mlsanity.com/paper/2603.12442/) (2026)
- [PathRIR: Physics-Guided Acoustic Path Selection and Late-Tail Compensation for Fast Room Impulse Response Simulation](https://mlsanity.com/paper/2607.23293/) (2026)
- [FFASR: Benchmarking Far-Field Automatic Speech Recognition using High-Fidelity Simulated RIRs](https://mlsanity.com/paper/2609.38897/) (2026)

## Abstract (arXiv)

Audio-signal-processing and audio-machine-learning (ASP/AML) algorithms are ubiquitous in modern technology like smart devices, wearables, and entertainment systems. Development of such algorithms and models typically involves a formal evaluation to demonstrate their effectiveness and progress beyond the state-of-the-art. Ideally, a thorough evaluation should cover many diverse application scenarios and room-acoustic conditions. However, in practice, evaluation datasets are often limited in size and diversity because they rely on costly and time-consuming measurements. This paper explores how room-acoustic simulations can be used for evaluating ASP/AML algorithms. To this end, we evaluate three ASP/AML algorithms with room-acoustic measurements and data from different simulation engines, and assess the match between the evaluation results obtained from measurements and simulations. The presented investigation compares a numerical wave-based solver with two geometrical acoustics simulators. While numerical wave-based simulations yielded similar evaluation results as measurements for all three evaluated ASP/AML algorithms, geometrical acoustic simulations could not replicate the measured evaluation results as reliably.