Datasets:
File size: 3,966 Bytes
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license: cc-by-4.0
task_categories:
- audio-classification
language:
- en
pretty_name: A-TRE-10k
size_categories:
- 10K<n<100K
tags:
- audio
- compositionality
- benchmark
- dx7
- icassp2026
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 32000
- name: metadata
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: 640126866
num_examples: 1000
- name: train
num_bytes: 5121009000
num_examples: 8000
- name: val
num_bytes: 640126266
num_examples: 1000
download_size: 6401715241
dataset_size: 6401262132
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: train
path: data/train-*
- split: val
path: data/val-*
---
# A-TRE-10k
[](https://arxiv.org/abs/2603.13685)
[](https://github.com/chuyangchencd/audio-compositionality)
[](https://huggingface.co/datasets/chuyangchenn/a-coat-2k)
**A**udio **T**ree **R**econstruction **E**rror benchmark — 10,000 synthetic audio scenes
for evaluating whether audio encoders represent multi-source scenes compositionally.
Companion dataset to the ICASSP 2026 paper [*Evaluating Compositional Structure in Audio
Representations*](https://arxiv.org/abs/2603.13685). See also the
zero-shot benchmark [`chuyangchenn/a-coat-2k`](https://huggingface.co/datasets/chuyangchenn/a-coat-2k).
## Quick start
```python
from datasets import load_dataset
ds = load_dataset("chuyangchenn/a-tre-10k", split="train") # or "val", "test"
ex = ds[0]
samples = ex["audio"].get_all_samples()
waveform = samples.data # torch.Tensor, shape (1, 320000)
sr = samples.sample_rate # 32000
metadata = ex["metadata"] # list of {timbre_label, pitch_label, rate_label, amplitude_label}
```
Streaming (no local download):
```python
ds = load_dataset("chuyangchenn/a-tre-10k", split="test", streaming=True)
for ex in ds.take(5):
print(ex["audio"].get_all_samples().data.shape)
```
## Dataset structure
Each row is one 10-second 32 kHz mono audio scene plus its source-attribute metadata.
| Field | Type | Description |
|------------|-------------------------------|--------------------------------------------------------------|
| `audio` | `Audio(sampling_rate=32000)` | Waveform, shape `(1, 320000)` — peak-normalised mono. |
| `metadata` | `list[dict]` | One entry per source: `{timbre_label, pitch_label, rate_label, amplitude_label}`. |
A scene contains **N ∈ {1, 2, 3, 4}** independent sources, each described by 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
## Splits
| Split | # scenes |
|-------|---------:|
| train | 8,000 |
| val | 1,000 |
| test | 1,000 |
## 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.
|