README.md

---
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

[![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-COAT-2k](https://img.shields.io/badge/🤗-A--COAT--2k-yellow)](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.