AES E-Library

← Back to search

A Magnitude-Based Parametric Model Predicting the Audibility of HRTF Variation

This work proposes a parametric model for just noticeable differences of unilateral differences in head-related transfer functions (HRTFs). For seven generic magnitude-based distance metrics, common trends in their response to inter-individual and intra-individual HRTF differences are analyzed, identifying metric subgroups with pseudo-orthogonal behavior. On the basis of three representative metrics, a three-alternative forced-choice experiment is conducted, and the acquired discrimination probabilities are set in relation with distance metrics via different modeling approaches. A linear model, with coefficients based on principal component analysis and three distance metrics as input, yields the best performance, compared to a simple multi-linear regression approach or to principal component analysis--based models of higher complexity.


Author (s):
Affiliation: (See document for exact affiliation information.)
Publication Date:


Click to purchase paper as a non-member or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member Join the AES. If you need to check your member status, login to the Member Portal.

E-Libary location:
Choose your country of residence from this list:

Skip to content