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README.md

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----
-license: mit
----
+---
+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

metadata/extraction_stats.json

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+{
+  "total_tracks": 150,
+  "total_segments": 16910,
+  "stems_extracted": {
+    "mixture": 16910,
+    "drums": 16910,
+    "bass": 16910,
+    "accompaniment": 16910,
+    "vocals": 16910
+  },
+  "failed_tracks": []
+}

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