Binaqual

Python implementation of BINAQUAL full-reference localization similarity metric for binaural audio, as presented in the following paper:

Shariat Panah, D., Barry, D., Ragano, A., Skoglund, J., & Hines, A. (2025). BINAQUAL: A Full-Reference Objective Localization Similarity Metric for Binaural Audio. https://doi.org/10.48550/arXiv.2505.11915

Setup

Install the required packages by running:

pip install -r requirements.txt

Usage

The program can be used by running:

python -m binaqual --ref /path/to/dir/reference_signal --deg /path/to/dir/test_sginal

Model validation

To validate the BINAQUAL metric, first download the SynBAD dataset from the following link and extract it in the main directory: https://zenodo.org/records/15431990

Then, run the model_validation.py script under the validation directory to apply Binaqual on the SynBAD dataset, as used in the paper's experiments. The results can then be plotted using the plots.py script.

Citation

If you use this code, please cite the associated paper:

@misc{panah2025binaqualfullreferenceobjectivelocalization,
      title={BINAQUAL: A Full-Reference Objective Localization Similarity Metric for Binaural Audio}, 
      author={Davoud Shariat Panah and Dan Barry and Alessandro Ragano and Jan Skoglund and Andrew Hines},
      year={2025},
      eprint={2505.11915},
      archivePrefix={arXiv},
      primaryClass={eess.AS},
      url={https://arxiv.org/abs/2505.11915}, 
}

If you use the SynBAD dataset, cite the above paper and the Binamix library paper:

@misc{barry2025binamixpythonlibrary,
      title={Binamix - A Python Library for Generating Binaural Audio Datasets}, 
      author={Dan Barry and Davoud Shariat Panah and Alessandro Ragano and Jan Skoglund and Andrew Hines},
      year={2025},
      eprint={2505.01369},
      journal={arXiv preprint arXiv:2505.01369},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2505.01369}, 
}

Licence

This project is licensed under the Apache 2.0 License.