README.md

---
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
    dtype:
      audio:
        sampling_rate: 32000
  - name: B
    dtype:
      audio:
        sampling_rate: 32000
  - name: C
    dtype:
      audio:
        sampling_rate: 32000
  - name: D
    dtype:
      audio:
        sampling_rate: 32000
  - name: metadata
    struct:
    - name: A
      list:
      - name: timbre_label
        dtype: string
      - name: pitch_label
        dtype: string
      - name: rate_label
        dtype: string
      - name: amplitude_label
        dtype: string
    - name: C
      list:
      - name: timbre_label
        dtype: string
      - name: pitch_label
        dtype: string
      - name: rate_label
        dtype: string
      - name: amplitude_label
        dtype: string
    - name: T
      list:
      - name: timbre_label
        dtype: string
      - name: pitch_label
        dtype: string
      - name: rate_label
        dtype: string
      - name: amplitude_label
        dtype: string
  splits:
  - name: test
    num_bytes: 5120679688
    num_examples: 2000
  download_size: 5121174847
  dataset_size: 5120679688
configs:
- config_name: default
  data_files:
  - 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.