---
license: cc-by-4.0
task_categories:
- audio-classification
tags:
- spatial-audio
- room-impulse-response
- audio-representation-learning
- probing
pretty_name: SARL Spatial Audio RIRs
---

# SARL — Spatial Audio RIRs

Room impulse responses for the **SARL** benchmark (*Probing Spatial Structure in
Pretrained Audio Representations*, accepted at Interspeech 2026;
[arXiv:2606.05544](https://arxiv.org/abs/2606.05544)). Code and full instructions:
**https://github.com/chuyangchencd/SARL**

This holds **only the RIRs**. The source clips (`audio/`) and ambient noise (`ambient/`)
can't be redistributed, so you build them from public datasets following the code repo.

## Contents

The RIRs ship as 18 tar archives — one per (family × split × format) — plus a small
`metadata.json` per split (not tarred, so it's directly browsable):

```
rir_source_<split>_<fmt>.tar     source-task RIRs (ray-traced) -> rir_source/<split>/<fmt>/NNNNN.npy
rir_room_<split>_<fmt>.tar       room-task RIRs (shoebox)      -> rir_room/<split>/<fmt>/NNNNN.wav
rir_source/<split>/metadata.json   one per split, shared by all formats
rir_room/<split>/metadata.json     one per split, shared by all formats
```

- `<split>` ∈ `train` / `val` / `test` (rooms disjoint across splits).
- `<fmt>` is the stored capture format: **`mic`** (4-channel tetrahedral; downmixed
  to stereo on load), **`binaural`** (2-channel), **`foa`** (4-channel first-order
  Ambisonics, **ACN/SN3D — AmbiX**). Every scene is provided in all three.
- **`rir_source`** — `.npy` of shape `[C, N, T]` (N candidate source positions); metadata
  gives per-position `coordinates` (azimuth −180–180°, elevation −60–60°, distance
  0.5–2.5 m) and `space_name`.
- **`rir_room`** — `.wav` of shape `[C, T]`; metadata gives `rt60` (s), `volume` (m³),
  and `shape` (0–3 = cube/flat/corridor/rectangular).

## Usage

Download and unpack the tars in place:

```bash
huggingface-cli download chuyangchenn/SARL --repo-type dataset --local-dir data_root
cd data_root && for t in rir_*.tar; do tar xf "$t"; done && rm rir_*.tar
```

To fetch only the capture format you need (e.g. `foa`), add
`--include "*_foa.tar" "*/metadata.json" "README.md"` to the download.

Then build `audio/` and `ambient/` into the same `data_root` (see the code repo) and
run the benchmark. These RIRs are the exact ones used in the paper.

## Citation

```bibtex
@article{chen2026sarl,
  title   = {Probing Spatial Structure in Pretrained Audio Representations},
  author  = {Chen, Chuyang and Ding, Sivan and Roman, Adrian S. and Bello, Juan P.},
  journal = {arXiv preprint arXiv:2606.05544},
  year    = {2026},
}
```