Disentangling subject-specific features and location-specific features from Head Related Transfer Functions

Zurale D, Lu L, Seefeldt A. Disentangling subject-specific features and location-specific features from Head Related Transfer Functions. In: AES 2024 International Conference on Audio for Virtual and Augmented Reality. Audio Engineering Society; 2024. Paper 6. Available from: https://aes.org/publications/elibrary-page/?id=22655

@inproceedings{Zurale2024_22655,
  author = {Zurale, Devansh and Lu, Lie and Seefeldt, Alan},
  title = {{Disentangling subject-specific features and location-specific features from Head Related Transfer Functions}},
  booktitle = {AES 2024 International Conference on Audio for Virtual and Augmented Reality},
  note = {Paper 6},
  year = {2024},
  month = aug,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=22655}
}

TY  - CPAPER
TI  - Disentangling subject-specific features and location-specific features from Head Related Transfer Functions
AU  - Zurale, Devansh
AU  - Lu, Lie
AU  - Seefeldt, Alan
T2  - AES 2024 International Conference on Audio for Virtual and Augmented Reality
M1  - Paper 6
PY  - 2024
DA  - 2024/08/05
UR  - https://aes.org/publications/elibrary-page/?id=22655
PB  - Audio Engineering Society
LA  - en
AB  - In this study we propose a novel approach for the latent representation of the Head Related Transfer Functions (HRTFs), which are understood to be dominant cues for the perception of sound localization. HRTFs represent the acoustic transformations that a sound signal undergoes between its point of origin in space and its receiving point inside the ear, due to interactions with the human outer body structures such as the torso, head and the pinna, thus making the HRTFs a function of both the subjects anthropometric features as well as of the source locations, for a given individual subject. In this work we propose to derive latent representations of the HRTFs by disentangling subject-specific features from the location-specific features. We posit that such latent representations hold promise for personalized HRTF predictions in various downstream applications. We validate the disentanglement of the features through visualizations, comparing the disentangled latent representations with those without the disentan-glement. Additionally, we also conduct a comprehensive analysis to understand the impact of disentanglement on the reconstructions.
ER  -