Datasets:
File size: 4,636 Bytes
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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
[](https://arxiv.org/abs/2603.13685)
[](https://github.com/chuyangchencd/audio-compositionality)
[](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.
|