Update README.md

README.md

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 license: mit
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+<div align="center">
+  <h1>CoughPhase-CLR</h1>
+  <p><em>An acoustics-informed foundation model for coughing sound classification</em></p>
+</div>
+
+
+CoughPhase-CLR is a self-supervised foundation model for coughing sound classification. Unlike generic contrastive frameworks that build positive pairs through random cropping, it constructs positive pairs from the distinct **physiological phases of a single cough** (explosive phase vs. intermediate + voiced phases). Built on OPERA-CE (an EfficientNet-B0 encoder trained with a contrastive objective), it is pretrained on ~40 hours of public cough audio and evaluated on five downstream tasks spanning COVID-19 detection, COPD-state classification, gender, and smoker-status prediction. Phase-aware pretraining consistently outperforms standard random cropping on coughs and is more data-efficient.
+<div align="center">
+  <img width=80% src="https://cdn-uploads.huggingface.co/production/uploads/68643b761bd37a5e3db83c0f/PxzBkJxuW6JXQbZXiy2aw.png">
+</div>
+
+
+The code is available at: https://github.com/your-repo/CoughPhase-CLR
+
+## Citation
+
+Kindly cite our work if you find it useful.
+```
+@misc{moldovan2025coughphaseclr,
+      title={CoughPhase-CLR: Designing an acoustics-informed foundation model for coughing sound classification},
+      author={Marius Moldovan and Anton Batliner and Thomas M. Berghaus and Bj\"orn W. Schuller and Andreas Triantafyllopoulos},
+      year={2026},
+}
+```