Add dataset card

README.md

@@ -1,4 +1,19 @@
 ---
+license: cc-by-4.0
+task_categories:
+- audio-classification
+language:
+- en
+pretty_name: A-COAT-2k
+size_categories:
+- 1K<n<10K
+tags:
+- audio
+- compositionality
+- benchmark
+- dx7
+- icassp2026
+- zero-shot
 dataset_info:
   features:
   - name: A
@@ -61,3 +76,84 @@ configs:
   - split: test
     path: data/test-*
 ---
+
+# A-COAT-2k
+
+[![arXiv](https://img.shields.io/badge/arXiv-2603.13685-b31b1b.svg)](https://arxiv.org/abs/2603.13685)
+[![Code](https://img.shields.io/badge/Code-GitHub-181717?logo=github)](https://github.com/chuyangchencd/audio-compositionality)
+[![Companion: A-TRE-10k](https://img.shields.io/badge/🤗-A--TRE--10k-yellow)](https://huggingface.co/datasets/chuyangchenn/a-tre-10k)
+
+**A**udio **C**ompositional **O**bject **A**lgebra **T**est — 2,000 zero-shot audio
+quadruples for evaluating whether audio encoders represent multi-source scenes
+compositionally. **No training required.**
+
+Companion dataset to the ICASSP 2026 paper [*Evaluating Compositional Structure in Audio
+Representations*](https://arxiv.org/abs/2603.13685). See also the
+trained-head benchmark [`chuyangchenn/a-tre-10k`](https://huggingface.co/datasets/chuyangchenn/a-tre-10k).
+
+## Quick start
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("chuyangchenn/a-coat-2k", split="test")
+ex = ds[0]
+A = ex["A"].get_all_samples().data   # torch.Tensor, shape (1, 320000)
+B = ex["B"].get_all_samples().data   # B = A ∪ T
+C = ex["C"].get_all_samples().data
+D = ex["D"].get_all_samples().data   # D = C ∪ T
+metadata = ex["metadata"]            # {"A": [...], "C": [...], "T": [...]}
+```
+
+## What's a quadruple?
+
+Each row is a 4-tuple `(A, B, C, D)` where `B = A ∪ T` and `D = C ∪ T` — i.e. the
+**same** transformation set `T` is applied to two different base scenes. The
+benchmark score for an encoder `f` is the average over quadruples of:
+
+`A-COAT(A,B,C,D) = cos(f(B) − f(A), f(D) − f(C))`
+
+Score 1 = adding `T` shifts the embedding by the same vector regardless of base scene
+(perfect compositionality). Random encoders score ≈ 0.
+
+## Dataset structure
+
+| Field      | Type                          | Description                                  |
+|------------|-------------------------------|----------------------------------------------|
+| `A`,`B`,`C`,`D` | `Audio(sampling_rate=32000)` | Waveforms, each `(1, 320000)` mono 32 kHz. |
+| `metadata` | `dict[str, list[dict]]`       | Source attributes per role: `A`, `C`, `T`. |
+
+Each source has four discrete attributes (K = 8 classes per attribute):
+
+- **timbre** — `t1`–`t8`: eight DX7 FM synth patches
+- **pitch** — `p1`–`p8`: MIDI 36–84, linearly binned
+- **rate** — `r1`–`r8`: 0.2–3.0 Hz, log-binned repetition rate
+- **amplitude** — `a1`–`a8`: −26 to 0 dB, linearly binned
+
+`A` and `C` each contain 1 source. `T` contains 1–3 sources (varies per quadruple).
+
+## Splits
+
+| Split | # quadruples |
+|-------|-------------:|
+| test  |        2,000 |
+
+(No train/val — A-COAT is a zero-shot benchmark.)
+
+## Citation
+
+```bibtex
+@inproceedings{chen2026audiocomp,
+  title         = {Evaluating Compositional Structure in Audio Representations},
+  author        = {Chen, Chuyang and Steers, Bea and McFee, Brian and Bello, Juan Pablo},
+  booktitle     = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+  year          = {2026},
+  eprint        = {2603.13685},
+  archivePrefix = {arXiv},
+  primaryClass  = {cs.SD}
+}
+```
+
+## License
+
+[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) — free use with attribution.