Binaspect: A Python Library for Binaural Audio Analysis, Visualization & Feature Generation

Barry D, Shariat Panah D, Ragano A, Skoglund J, Hines A. Binaspect: A Python Library for Binaural Audio Analysis, Visualization & Feature Generation. In: Convention Paper. Audio Engineering Society; 2026. Paper 10279. Available from: https://aes.org/publications/elibrary-page/?id=23218

@inproceedings{Barry2026_23218,
  author = {Barry, Dan and Shariat Panah, Davoud and Ragano, Alessandro and Skoglund, Jan and Hines, Andrew},
  title = {{Binaspect: A Python Library for Binaural Audio Analysis, Visualization \& Feature Generation}},
  note = {Paper 10279},
  year = {2026},
  month = may,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=23218}
}

TY  - CPAPER
TI  - Binaspect: A Python Library for Binaural Audio Analysis, Visualization & Feature Generation
AU  - Barry, Dan
AU  - Shariat Panah, Davoud
AU  - Ragano, Alessandro
AU  - Skoglund, Jan
AU  - Hines, Andrew
M1  - Paper 10279
PY  - 2026
DA  - 2026/05/28
UR  - https://aes.org/publications/elibrary-page/?id=23218
PB  - Audio Engineering Society
LA  - en
AB  - We present Binaspect, an open-source Python library for binaural audio analysis, visualization, and feature generation. Binaspect generates interpretable azimuth maps by calculating modified interaural time and level difference spectrograms, and clustering those time-frequency (TF) bins into stable time-azimuth histogram representations. This allows multiple active sources to appear as distinct azimuthal clusters, while degradations manifest as broadened, diffused, or shifted distributions. Crucially, Binaspect operates blindly on audio, requiring no prior knowledge of head models. These visualizations enable researchers and engineers to observe how binaural cues are degraded by codec and renderer design choices, among other downstream processes. We demonstrate the tool on bitrate ladders, ambisonic rendering, and VBAP source positioning, where degradations are clearly revealed. In addition to their diagnostic value, the proposed representations can be exported as structured features suitable for training machine learning models in quality prediction, spatial audio classification, and other binaural tasks. Binaspect is released under an open-source license with full reproducibility scripts at: https://github.com/QxLabIreland/Binaspect.
ER  -