Eigen-Images of Head-Related Transfer Functions

Hold C, Seipel F, Brinkmann F, Lykartsis A, Weinzierl S. Eigen-Images of Head-Related Transfer Functions. In: AES Convention 143. Audio Engineering Society; 2017. Paper 9891. Available from: https://aes.org/publications/elibrary-page/?id=19288

@inproceedings{Hold2017_19288,
  author = {Hold, Christoph and Seipel, Fabian and Brinkmann, Fabian and Lykartsis, Athanasios and Weinzierl, Stefan},
  title = {{Eigen-Images of Head-Related Transfer Functions}},
  booktitle = {AES Convention 143},
  note = {Paper 9891},
  year = {2017},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=19288}
}

TY  - CPAPER
TI  - Eigen-Images of Head-Related Transfer Functions
AU  - Hold, Christoph
AU  - Seipel, Fabian
AU  - Brinkmann, Fabian
AU  - Lykartsis, Athanasios
AU  - Weinzierl, Stefan
T2  - AES Convention 143
M1  - Paper 9891
PY  - 2017
DA  - 2017/10/06
UR  - https://aes.org/publications/elibrary-page/?id=19288
PB  - Audio Engineering Society
LA  - en
AB  - The individualization of head-related transfer functions (HRTFs) leads to perceptually enhanced virtual environments. Particularly the peak-notch structure in HRTF spectra depending on the listener’s specific head and pinna anthropometry contains crucial auditive cues, e.g., for the perception of sound source elevation. Inspired by the eigen-faces approach, we have decomposed image representations of individual full spherical HRTF data sets into linear combinations of orthogonal eigen-images by principle component analysis (PCA). Those eigen-images reveal regions of inter-subject variability across sets of HRTFs depending on direction and frequency. Results show common features as well as spectral variation within the individual HRTFs. Moreover, we can statistically de-noise the measured HRTFs using dimensionality reduction.
ER  -