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.
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