| --- |
| 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}, |
| } |
| ``` |
|
|
