Binamix - A Python Library for Generating Binaural Audio Datasets

Barry D, Panah DS, Ragano A, Skoglund J, Hines A. Binamix - A Python Library for Generating Binaural Audio Datasets. In: Express Paper. Audio Engineering Society; 2025. Paper 319. Available from: https://aes.org/publications/elibrary-page/?id=22870

@inproceedings{Barry2025_22870,
  author = {Barry, Dan and Panah, Davoud Shariat and Ragano, Alessandro and Skoglund, Jan and Hines, Andrew},
  title = {{Binamix - A Python Library for Generating Binaural Audio Datasets}},
  note = {Paper 319},
  year = {2025},
  month = may,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=22870}
}

TY  - CPAPER
TI  - Binamix - A Python Library for Generating Binaural Audio Datasets
AU  - Barry, Dan
AU  - Panah, Davoud Shariat
AU  - Ragano, Alessandro
AU  - Skoglund, Jan
AU  - Hines, Andrew
M1  - Paper 319
PY  - 2025
DA  - 2025/05/12
UR  - https://aes.org/publications/elibrary-page/?id=22870
PB  - Audio Engineering Society
LA  - en
AB  - The increasing demand for spatial audio in applications such as virtual reality, immersive media, and spatial audio research necessitates robust solutions for binaural audio dataset generation for testing and validation. Binamix is an open-source Python library designed to facilitate programmatic binaural mixing using the extensive SADIE II Database, which provides HRIR and BRIR data for 20 subjects. The Binamix library provides a flexible and repeatable framework for creating large-scale spatial audio datasets, making it an invaluable resource for codec evaluation, audio quality metric development, and machine learning model training. A range of pre-built example scripts, utility functions, and visualization plots further streamline the process of custom pipeline creation. This paper presents an overview of the library's capabilities, including binaural rendering, impulse response interpolation, and multi-track mixing for various speaker layouts. The tools utilize a modified Delaunay triangulation technique to achieve accurate HRIR/BRIR interpolation where desired angles are not present in the data. By supporting a wide range of parameters such as azimuth, elevation, subject IRs, speaker layouts, mixing controls, and more, the library enables researchers to create large binaural datasets for any downstream purpose. Binamix empowers researchers and developers to advance spatial audio applications with reproducible methodologies by offering an open-source solution for binaural rendering and dataset generation.
ER  -