Title:
Quantitatively Validating Subjectively Selected HRTFs for Elevation and Front-Back Distinction
Quantitatively Validating Subjectively Selected HRTFs for Elevation and Front-Back Distinction
Author(s)
Fan, Ziqi
Wan, Yunhao
McMullen, Kyla
Wan, Yunhao
McMullen, Kyla
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Abstract
As 3D audio becomes more commonplace to enhance auditory environments, designers are faced with the challenge of choosing HRTFs for listeners that provide proper audio cues. Subjective selection is a low-cost alternative to expensive HRTF measurement,
however little is known concerning whether the preferred HRTFs are similar or if users exhibit random behavior in this task. In addition, PCA (principal component analysis) can be used to decompose HRTFs in representative features, however little is known
concerning whether the features have a relevant perceptual basis. 12 listeners completed a subjective selection experiment in which they judged the perceptual quality of 14 HRTFs in terms of elevation, and front-back distinction. PCA was used to decompose
the HRTFs and create an HRTF similarity metric. The preferred HRTFs were significantly more similar to each other, the preferred and non-preferred HRTFs were significantly less similar to each other, and in the case of front-back distinction the non-preferred
HRTFs were significantly more similar to each other.
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Date Issued
2016-07
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.