The significant amount of variance in head-related transfer functions (HRTFs) resulting from source location and subject dependencies have led researchers to use principal components analysis (PCA) to approximate HRTFs with a small set of basis functions. PCA minimizes a mean-square error, and consequently may spend modeling effort on perceptually irrelevant properties. To investigate the extent of this effect, PCA performance was studied before and after removal of perceptually irrelevant variance. The results indicate that from the sixth PCA component onward, a substantial amount of perceptually irrelevant variance is being accounted for.
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