---
license: mit
task_categories:
- audio-classification
- audio-to-audio
tags:
- audio
- music
- source-separation
- musdb18
- stems
- active-segments
- cs229
- stanford
pretty_name: "MUSDB18 Active Stems - CS229 Project"
size_categories:
- 10K<n<100K
---

# MUSDB18 Active Stems Dataset - CS229 Project

This dataset contains active stem segments extracted from the MUSDB18 dataset for the Stanford CS229 Machine Learning course project on audio source separation.

## Dataset Description

This is a processed version of the MUSDB18 dataset containing only the active segments of each stem (drums, bass, vocals, accompaniment, and mixture), designed to improve training efficiency for music source separation models.

## Key Features

- **Active Segment Detection**: Only segments where stems have significant energy
- **5 Stems**: mixture, drums, bass, vocals, accompaniment
- **Consistent Format**: 22.05 kHz sample rate, mono audio
- **Rich Metadata**: Detailed segment information and statistics

## CS229 Project Context

This dataset was created as part of a Stanford CS229 course project focusing on:
- Music source separation using deep learning
- Comparison of different neural architectures (Conv-TasNet, etc.)
- Analysis of active vs. inactive audio segments in training

## Dataset Structure

```
extracted_stems/
├── train/              # Training split
│   ├── drums/         # Active drum segments
│   ├── bass/          # Active bass segments  
│   ├── vocals/        # Active vocal segments
│   ├── accompaniment/ # Active accompaniment segments
│   └── mixture/       # Active mixture segments
├── test/              # Test split (same structure)
└── metadata/          # JSON metadata files
```

## Quick Start

### Loading with datasets library
```python
from datasets import load_dataset

# Load the full dataset
dataset = load_dataset("cs229-audio-ml-project/musdb18-processed")

# Access training data
train_data = dataset["train"]
for item in train_data:
    audio = item["audio"]["array"]
    stem_type = item["stem_type"]
    track_name = item["track_name"]
```

### Manual loading
```python
import soundfile as sf
import json

# Load an audio segment
audio, sr = sf.read("train/vocals/track_vocals_001.wav")

# Load metadata
with open("metadata/train_metadata.json") as f:
    metadata = json.load(f)
```

## Extraction Parameters

- **Segment Length**: 4.0 seconds
- **Hop Length**: 2.0 seconds (50% overlap)
- **Energy Threshold**: 0.01 RMS
- **Sample Rate**: 22,050 Hz
- **Minimum Duration**: 1.0 seconds

## Citation

If you use this dataset in your research, please cite:

```bibtex
@dataset{cs229_musdb18_active_stems,
  title={MUSDB18 Active Stems Dataset},
  author={CS229 Audio ML Project Team},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/cs229-audio-ml-project/musdb18-processed}
}
```

Original MUSDB18 citation:
```bibtex
@misc{musdb18,
  author       = {Rafii, Zafar and Liutkus, Antoine and Stöter, Fabian-Robert and Mimilakis, Stylianos Ioannis and Bittner, Rachel},
  title        = {MUSDB18-HQ - an uncompressed version of MUSDB18},
  month        = {December},
  year         = {2019},
  doi          = {10.5281/zenodo.3338373},
  url          = {https://doi.org/10.5281/zenodo.3338373}
}
```

## Contact

For questions about this dataset or the CS229 project, please open an issue in this repository.

Created for Stanford CS229 - Machine Learning Course Project