---
license: cc-by-4.0
task_categories:
- audio-classification
- audio-to-audio
tags:
- audio
- music
- choral
- satb
- source-separation
- pytorch
pretty_name: SATB Choral Source Separation Dataset (Compressed)
size_categories:
- 1K<n<10K
---

# SATB Choral Source Separation Dataset (Compressed)

This dataset contains preprocessed 4-second audio chunks from the Choral Singing Dataset (CSD) formatted for SATB (Soprano, Alto, Tenor, Bass) voice source separation tasks.

**This is the compressed version with 8kHz sample rate and int8 precision for smaller file sizes.**

## Dataset Description

- **Total chunks**: 26,883
- **Dataset size**: ~4155.3 MB
- **Audio format**: 8 kHz mono, 4-second chunks  
- **Precision**: int8 for memory efficiency
- **Sources**: 4 individual voice parts (SATB)
- **Mixed audio**: Sum of all 4 voices

## Data Structure

### Folder Structure
```
├── chunks/                    # All individual chunk .pt files
├── quality_samples/           # Sample WAV files for quality checking
├── train_split.pt            # List of training chunk filenames
├── val_split.pt              # List of validation chunk filenames
└── test_split.pt             # List of test chunk filenames
```

### Chunk Files
Each `.pt` file in `chunks/` contains a dictionary with:
- `mixed`: Mixed audio tensor (4 seconds, 32k samples at 8kHz) - int8
- `sources`: Individual voice tensors [4, 32k] (soprano, alto, tenor, bass) - int8
- `song`: Song identifier ("ER", "LI", or "ND")
- `singer_combo`: List of singer IDs used [soprano_id, alto_id, tenor_id, bass_id]
- `chunk_id`: Chunk number within the combination

## Usage

```python
import torch

# Load data splits
train_files = torch.load('train_split.pt')
val_files = torch.load('val_split.pt')
test_files = torch.load('test_split.pt')

# Load a single chunk
chunk = torch.load(f'chunks/{train_files[0]}')
mixed_audio = chunk['mixed'].float() / 127.0  # Convert back to float
separated_voices = chunk['sources'].float() / 127.0

# mixed_audio shape: [32000] (4 seconds at 8kHz)
# separated_voices shape: [4, 32000] (SATB voices)
```

## Compression Details

- **Sample rate**: Reduced from 16kHz to 8kHz (2x reduction)
- **Precision**: int8 instead of float16 (2x reduction)  
- **Total compression**: ~4x smaller than original
- **Quality samples**: Available in `quality_samples/` for evaluation

## Original Dataset

Based on the Choral Singing Dataset (CSD):
- **Songs**: "Erbarme dich" (ER), "Locus iste" (LI), "Nuper rosarum flores" (ND)
- **Singers**: 4 different singers per voice part
- **Combinations**: All possible singer combinations (4^4 = 256 per song)

## Citation

If you use this dataset, please cite the original CSD paper and this preprocessing work.

## License

CC-BY-4.0 - Same as the original Choral Singing Dataset