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---
title: MAPSS Multi Source Audio Perceptual Separation Scores
emoji: 🎵
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.45.0
app_file: app.py
pinned: false
license: mit
---
# MAPSS: Manifold-based Assessment of Perceptual Source Separation
Granular evaluation of speech and music source separation with the MAPSS measures:
- **Perceptual Matching (PM)**: Measures how closely an output perceptually aligns with its reference. Range: 0-1, higher is better.
- **Perceptual Similarity (PS)**: Measures how well an output is separated from its interfering references. Range: 0-1, higher is better.
## Input Format
Upload a ZIP file containing:
```
your_mixture.zip
├── references/ # Original clean sources
│ ├── speaker1.wav
│ ├── speaker2.wav
│ └── ...
└── outputs/ # Separated outputs from your algorithm
├── separated1.wav
├── separated2.wav
└── ...
```
### Audio Requirements
- Format: WAV files
- Sample rate: Any (automatically resampled to 16kHz)
- Channels: Mono or stereo (converted to mono)
- Number of files: Equal number of references and outputs
## Output Format
The tool generates a ZIP file containing:
- `ps_scores_{model}.csv`: PS scores for each speaker/source
- `pm_scores_{model}.csv`: PM scores for each speaker/source
- `params.json`: Experiment parameters used
- `manifest_canonical.json`: File mapping and processing details
## Available Models
| Model | Description | Default Layer | Use Case |
|-------|-------------|---------------|----------|
| `raw` | Raw waveform features | N/A | Baseline comparison |
| `wavlm` | WavLM Large | 24 | Best overall performance |
| `wav2vec2` | Wav2Vec2 Large | 24 | Strong performance |
| `hubert` | HuBERT Large | 24 | Good for speech |
| `wavlm_base` | WavLM Base | 12 | Faster, good quality |
| `wav2vec2_base` | Wav2Vec2 Base | 12 | Faster processing |
| `hubert_base` | HuBERT Base | 12 | Faster for speech |
| `wav2vec2_xlsr` | Wav2Vec2 XLSR-53 | 24 | Multilingual |
| `ast` | Audio Spectrogram Transformer | 12 | General audio |
## Parameters
- **Model**: Select the embedding model for feature extraction
- **Layer**: Which transformer layer to use (auto-selected by default)
- **Alpha**: Diffusion maps parameter (0.0-1.0, default: 1.0)
- 0.0 = No normalization
- 1.0 = Full normalization (recommended)
## Citation
If you use MAPSS in your research, please cite:
```bibtex
@article{Ivry2025MAPSS,
title = {MAPSS: Manifold-based Assessment of Perceptual Source Separation},
author = {Ivry, Amir and Cornell, Samuele and Watanabe, Shinji},
journal = {arXiv preprint arXiv:2509.09212},
year = {2025},
url = {https://arxiv.org/abs/2509.09212}
}
```
## Limitations
- Processing time scales with number of sources, audio length and model size
## License
Code: MIT License
Paper: CC-BY-4.0
## Support
For issues, questions, or contributions, please visit the [GitHub repository](https://github.com/amir-ivry/MAPSS-measures). |