Quantitatively Validating Subjectively Selected HRTFs for Elevation and Front-Back Distinction
Ziqi Fan,Yunhao Wan,Kyla McMullen +2 more
- pp 158-165
TLDR
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, however little is known concerning whether the features have a relevant perceptual basis.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.read more
Citations
More filters
Journal ArticleDOI
A machine learning tutorial for spatial auditory display using head-related transfer functions.
Kyla McMullen,Yu-Ting Wan +1 more
TL;DR: This work addresses the use of ML techniques such as dimensionality reduction, unsupervised learning, supervised learning, reinforcement learning, and deep learning algorithms to address specific spatial auditory display research challenges.
Journal Article
Repeatability of Localization Cues in HRTF Data Bases
Elena Blanco-Martin,Silvia Merino Saez-Miera,Juan Jose Gomez-Alfageme,Luis I. Ortiz-Berenguer +3 more
Journal ArticleDOI
Use of k-means clustering analysis to select representative head related transfer functions for use in subjective studies
TL;DR: In this paper, the authors developed a listening test to identify a "matched" and "unmatched" head related transfer function (HRTF) for specific subjects, which could be applied to customize auralizations for individual participants.
References
More filters
Proceedings ArticleDOI
k-means++: the advantages of careful seeding
TL;DR: By augmenting k-means with a very simple, randomized seeding technique, this work obtains an algorithm that is Θ(logk)-competitive with the optimal clustering.
Proceedings ArticleDOI
The CIPIC HRTF database
TL;DR: A public-domain database of high-spatial-resolution head-related transfer functions measured at the UC Davis CIPIC Interface Laboratory and the methods used to collect the data are described.
Journal ArticleDOI
Localization using nonindividualized head‐related transfer functions
TL;DR: Data suggest that while the interaural cues to horizontal location are robust, the spectral cues considered important for resolving location along a particular cone-of-confusion are distorted by a synthesis process that uses nonindividualized HRTFs.
Journal Article
Binaural Technique: Do We Need Individual Recordings?
TL;DR: In this paper, the localization performance was studied when subjects listen to a real sound field and to binaural recordings of the same sound field, made in their own ears and in the ears of other subjects.
Related Papers (5)
Usability of Individualized Head-Related Transfer Functions in Virtual Reality: Empirical Study With Perceptual Attributes in Sagittal Plane Sound Localization
Claudia Jenny,Christoph Reuter +1 more