Update model card: fix paper link, add pipeline tag and usage (#1)

- Update model card: fix paper link, add pipeline tag and usage (44eb7a9816d9c9a60c2fd8091f46715c48d83cd1)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,7 +1,6 @@
 ---
-papers:
-- https://arxiv.org/abs/2501.01234
 license: mit
+pipeline_tag: audio-to-audio
 tags:
 - beamforming
 - audio
@@ -10,18 +9,51 @@ tags:
 
 # Cyclic MVDR Beamforming
 
-Implementation for  
-> Giovanni Bologni, Martin Bo Møller, Richard Heusdens, Richard C. Hendriks.  
-> **MVDR Beamforming for Cyclostationary Processes**, arXiv:2510.18391.
+Python implementation of the cyclic MVDR beamformer (also known as cyclic minimum power distortionless response, cMPDR) — a model-based algorithm for cancelling dominant **almost-cyclostationary** (ACS) interferers such as engines, fans, and musical instruments.
+
+This implementation accompanies the paper: [MPDR Beamforming for Almost-Cyclostationary Processes](https://huggingface.co/papers/2510.18391).
 
 📄 **Paper**: [https://arxiv.org/abs/2510.18391](https://arxiv.org/abs/2510.18391)  
-💻 **Code**: [https://github.com/Screeen/cMVDR](https://github.com/Screeen/cMVDR)
+💻 **Code**: [https://github.com/Screeen/cmvdr](https://github.com/Screeen/cmvdr)
 
 ---
 
 ## Description
-Python implementation of the cyclic MVDR beamformer — an extension of the classic MVDR that exploits both spatial and spectral correlations to better suppress almost-periodic noise (e.g., engines, fans, musical instruments).
-
-By exploiting correlations across microphones and frequency components, the cyclic minimum-variance distortionless-response (cMVDR) beamformer achieves improved noise reduction, especially in low signal-to-noise ratio (SNR) scenarios. The package includes tools for estimating resonant frequencies via periodogram analysis and computing optimal frequency shifts for inharmonic signals.
+Conventional acoustic beamformers typically assume short-time stationarity and process frequency bins independently, ignoring inter-frequency correlations. The cyclic minimum power distortionless response (cMPDR) beamformer extends the conventional MPDR to jointly exploit spatial and spectral correlations. By suppressing noise components that are coherent across harmonically related frequencies, it achieves improved noise reduction, especially in low signal-to-noise ratio (SNR) scenarios.
 
 Applicable to speech enhancement, hearing aids, smart devices, and acoustic scene analysis.
+
+## Sample Usage
+
+### Installation
+The package can be installed after cloning the repository:
+```bash
+git clone https://github.com/Screeen/cmvdr.git
+cd cmvdr
+pip install -e .
+```
+
+### Command Line Interface
+After installation, you can use the `cmvdr` command line tool to process audio files:
+```bash
+# Run cMPDR inference on a single file or a folder
+cmvdr -i path/to/input_audio.wav -o path/to/output_folder/
+```
+
+### Python Usage
+You can also import the package in your Python scripts:
+```python
+import cmvdr
+print("cMVDR package successfully imported!")
+```
+
+## Citation
+If you find this work useful, please cite:
+```bibtex
+@article{bologni2025mpdr,
+  title={MPDR Beamforming for Almost-Cyclostationary Processes},
+  author={Bologni, Giovanni and M{\o}ller, Martin Bo and Heusdens, Richard and Hendriks, Richard C.},
+  journal={arXiv preprint arXiv:2510.18391},
+  year={2025}
+}
+```