# Bras Ir Eval

> Evaluates the accuracy of a hybrid acoustic simulation pipeline for generating room impulse responses (IRs) against real-world measured data. It specifically probes the model's ability to capture low-frequency diffraction effects and high-frequency energy decay in complex room geometries. Use when the user wants to benchmark on BRAS benchmark, or asks about evaluating this task. Reports frequency response.

- Skill: `qhjqhj00/bras-ir-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/bras-ir-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/bras-ir-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/bras-ir-eval

---


# bras-ir-eval

> GWA: A Large High-Quality Acoustic Dataset for Audio Processing — Tang et al. (2022) (arXiv:2204.01787, 2022)

## What this evaluates

Evaluates the accuracy of a hybrid acoustic simulation pipeline for generating room impulse responses (IRs) against real-world measured data. It specifically probes the model's ability to capture low-frequency diffraction effects and high-frequency energy decay in complex room geometries.

## Datasets

- **BRAS benchmark** — total 3; splits: test (3)

## Metrics

- `frequency response` **(primary)** — range: other
  - Compares simulated and measured IRs in the frequency domain, focusing on the alignment of frequency responses and energy decay trends across different room scenes.

## Input / output format

**Input**: Room geometry, source-listener locations, and material definitions (absorption and scattering coefficients).

**Output**: Generated room impulse responses (IRs) for the specified source-listener configurations.

## Scoring recipe

```python
Load measured IRs from BRAS benchmark scenes (RS5-7).
Load simulated IRs from hybrid pipeline.
Convert both IRs to frequency domain via FFT.
Compare frequency responses and energy decay curves.
Assess alignment and deviation trends visually/qualitatively.
```

## Common pitfalls

- The benchmark only provides absorption and scattering coefficients, not impedance data, requiring fitted values for wave solvers.
- Geometric methods alone fail at low frequencies due to diffraction, so hybrid simulation is required for accurate low-frequency modeling.
- Evaluation relies on frequency-domain visual comparison rather than a single scalar metric, making quantitative thresholding difficult.

## Evidence (verbatim from paper)

> The IRs generated by the GA method and our hybrid method and the measured IRs from the benchmark are compared in the frequency domain in Figure 6. In these scenes, the source and receiver are placed on different sides of the obstacle and the semi-anechoic room only has floor reflections. In the high frequency range, there are fewer variations in the measured response, and both methods capture the general trend of energy decay despite response levels not being perfectly matched.

## Citation

```bibtex
@misc{tang2022gwa,
  title={GWA: A Large High-Quality Acoustic Dataset for Audio Processing},
  author={Tang et al. (2022)},
  year={2022},
  note={arXiv:2204.01787}
}
```

- arXiv: 2204.01787