README: add arXiv link + citation
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README.md
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## Layout
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- `snr/cka_snr_<model>.parquet`
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the fine-tuning trajectory.
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- `diffusion_maps/` — pre-computed diffusion-map embeddings per (model, snr,
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noise, layer).
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##
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##
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<https://github.com/YairAmar/SE-Probe>. The `scripts/setup.py` helper there
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fetches this snapshot when invoked with `--full-data`.
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---
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license: mit
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tags:
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- speech-enhancement
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- interpretability
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- cka
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- probing
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---
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# SE-Probe full results dataset
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Pre-computed CKA, diffusion-map, and probing results for [SE-Probe](https://github.com/YairAmar/SE-Probe), the public code release for *"Where Does Speech Enhancement Adapt? Probing Study Under Controlled Degradation"* (Amar, Ivry, Cohen, 2026).
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📄 Paper: <https://arxiv.org/abs/2512.00482>
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## Layout
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- `snr/cka_snr_<model>.parquet`: per-architecture (MUSE, MP-SENet, Demucs) CKA values across additive-noise SNRs and DEMAND noise types, with per-row audio quality metrics (PESQ, STOI, SI-SDR, DNSMOS family).
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- `reverb/cka_reverb_muse_epoch_<N>.parquet`: per-epoch reverb-FT MUSE probing parquets. Epoch 48 is the final fine-tuned model; the others trace the fine-tuning trajectory.
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- `diffusion_maps/`: pre-computed diffusion-map embeddings per (model, snr, noise, layer).
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Total ~3.1 GB. To download into a local SE-Probe checkout: `python scripts/setup.py --full-data`.
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## Citation
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```bibtex
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@article{amar2026seprobe,
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title = {Where Does Speech Enhancement Adapt? Probing Study Under Controlled Degradation},
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author = {Amar, Yair and Ivry, Amir and Cohen, Israel},
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year = {2026},
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eprint = {2512.00482},
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archivePrefix = {arXiv},
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primaryClass = {eess.AS},
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url = {https://arxiv.org/abs/2512.00482}
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}
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```
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## License
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MIT.
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