---
dataset_info:
- config_name: rs
  features:
  - name: audio
    dtype: audio
  - name: id
    dtype: string
  - name: original_source
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  - name: num_repetitions
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  - name: events
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    dtype: string
  - name: spectrogram
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- config_name: rsn
  features:
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    dtype: audio
  - name: id
    dtype: string
  - name: original_source
    dtype: string
  - name: num_repetitions
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- config_name: rsn-features
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  - name: id
    dtype: string
  - name: spectrogram
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  - name: validation
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- config_name: rvn
  features:
  - name: audio
    dtype: audio
  - name: id
    dtype: string
  - name: original_source
    dtype: string
  - name: num_repetitions
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  - name: validation
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- config_name: rvn-features
  features:
  - name: id
    dtype: string
  - name: spectrogram
    dtype:
      array2_d:
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        dtype: float32
  - name: tssm
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  - name: validation
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    num_examples: 8340
  - name: test
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    num_examples: 20462
  download_size: 168149591715
  dataset_size: 167979200092
configs:
- config_name: rs
  data_files:
  - split: train
    path: rs/train-*
  - split: validation
    path: rs/validation-*
  - split: test
    path: rs/test-*
- config_name: rs-features
  data_files:
  - split: train
    path: rs-features/train-*
  - split: validation
    path: rs-features/validation-*
  - split: test
    path: rs-features/test-*
- config_name: rsn
  data_files:
  - split: train
    path: rsn/train-*
  - split: validation
    path: rsn/validation-*
  - split: test
    path: rsn/test-*
- config_name: rsn-features
  data_files:
  - split: train
    path: rsn-features/train-*
  - split: validation
    path: rsn-features/validation-*
  - split: test
    path: rsn-features/test-*
- config_name: rvn
  data_files:
  - split: train
    path: rvn/train-*
  - split: validation
    path: rvn/validation-*
  - split: test
    path: rvn/test-*
- config_name: rvn-features
  data_files:
  - split: train
    path: rvn-features/train-*
  - split: validation
    path: rvn-features/validation-*
  - split: test
    path: rvn-features/test-*
license: cc-by-nc-4.0
---
# Dataset Card for RepeatSynth

Synthetic portion of the audio datasets referenced in *Class-Agnostic Audio Repetition Counting*.

Code: https://github.com/Hamozwa/audio-counting

## Dataset Summary

RS, RSN, and RVN are synthetic datasets containing varying levels of background noise. Every sample is uniformly 10 seconds long and contains between 0 and 8 repetition events.

| Config | Description |
|---|---|
| `rs`  | RepeatSound — clean, no added background noise |
| `rsn` | RepeatSoundNoisy — background noise added |
| `rvn` | RepeatVeryNoisy — the noisiest condition |

Each config has `train`, `validation`, and `test` splits. `rs-features`, `rsn-features`, and `rvn-features` hold precomputed spectrograms and self-similarity matrices for the same clips (see "Feature configs" below) — most users only need the main configs above.

```python
from datasets import load_dataset
ds = load_dataset("Hamozwa/RepeatSynth", "rsn", split="train")
```

## Dataset Structure

| Field | Type | Description | Why it's here |
|---|---|---|---|
| `audio` | `Audio` | The 10-second wav clip | The actual training/test input |
| `id` | `string` | Original filename stem | Lets you cross-reference against the `-features` configs or reconstruct the original file layout |
| `original_source` | `string` | The FSD50K clip this sample's repeated event was built from | Required for attribution — FSD50K licenses each clip individually, so this is what you check to find the license of the audio actually used in a given sample |
| `num_repetitions` | `int` | Ground-truth repetition count (0–8) | The label WavCounter and TSSMCounter are trained/evaluated against |
| `events` | `list[{start: float, end: float}]` | Onset/offset (seconds) of each repeated event within the clip | Used to build the target heatmap WavCounter regresses against, rather than just a single count — this is what lets the model localize events in time, not just count them |

## Feature configs

`rs-features`/`rsn-features`/`rvn-features` contain `id`, `spectrogram`, and `tssm` (temporal self-similarity matrix) columns: the precomputed inputs TSSMCounter and spectrogram baseline actually train on, joinable back to the main config via `id`. These are included for convenience and exact reproducibility, but are fully derivable from the `audio` column above via the preprocessing code in the linked repo, so they're kept as separate configs rather than bundled into the main table.

## Dataset Sources

Synthetic clips were generated by combining two source collections:

- FSD50K — https://zenodo.org/records/4060432 — provided the audio for each repeated event
- MUSAN — http://openslr.org/ — used as background noise

## Licensing

This is a derivative work built from two source collections, each with different terms. Both require attribution.

**FSD50K** licenses each clip individually rather than as a whole collection (CC0, CC-BY, CC-BY-NC, or CC Sampling+, depending on the clip). Since every sample here is built from one specific FSD50K clip, the `original_source` field lets you look up that clip's exact license via FSD50K's own `dev_clips_info_FSD50K.json` / `eval_clips_info_FSD50K.json` mapping files. If you plan to use or redistribute a subset commercially, check the license of every clip referenced by `original_source` first — some are CC-BY-NC and restrict commercial reuse.

**MUSAN** is released under CC BY 4.0 (https://openslr.org/17/) — attribution required, otherwise unrestricted.

## Citation

```bibtex
[Your paper's BibTeX entry here]
```