README.md

# ArtifactNet-Real

Real (human-created) music dataset for AI-generated music detection research.

## Overview

| Split | Songs | Segments | Size |
|-------|-------|----------|------|
| Train | 15,546 | 70,463 | 47GB |
| Val | 1,943 | 8,075 | 5.4GB |
| Test | 1,944 | 8,384 | 5.6GB |
| **Total** | **19,433** | **86,922** | **~58GB** |

## Format

- **Audio**: WAV, 44.1kHz, Stereo
- **Segment Duration**: 4 seconds
- **Hop Size**: 2 seconds (50% overlap)

## Structure

```
ArtifactNet-Real/
├── README.md
├── metadata.json        # Song lists per split
├── train/{song_id}/seg_XXXX.wav
├── val/{song_id}/seg_XXXX.wav
└── test/{song_id}/seg_XXXX.wav
```

## Usage

This dataset provides **Real** (human-created) music samples for training AI music detection models.

To generate Demucs residuals:
```python
from demucs.pretrained import get_model
from demucs.apply import apply_model

model = get_model('htdemucs')
# residual = original - sum(sources)
```

## License

CC-BY-NC 4.0 (Non-commercial use)

## Citation

```bibtex
@dataset{artifactnet_real_2026,
  title={ArtifactNet-Real: Real Music Dataset for AI Detection},
  author={...},
  year={2026},
  url={...}
}
```

## Related

- **ArtifactNet**: AI-generated music detection framework
- **Paper**: [TBD]