Quality Taxonomies for Auditory Virtual Environments

Silzle A. Quality Taxonomies for Auditory Virtual Environments. In: AES Convention 122. Audio Engineering Society; 2007. Paper 6993. Available from: https://aes.org/publications/elibrary-page/?id=13978

@inproceedings{Silzle2007_13978,
  author = {Silzle, Andreas},
  title = {{Quality Taxonomies for Auditory Virtual Environments}},
  booktitle = {AES Convention 122},
  note = {Paper 6993},
  year = {2007},
  month = may,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=13978}
}

TY  - CPAPER
TI  - Quality Taxonomies for Auditory Virtual Environments
AU  - Silzle, Andreas
T2  - AES Convention 122
M1  - Paper 6993
PY  - 2007
DA  - 2007/05/06
UR  - https://aes.org/publications/elibrary-page/?id=13978
PB  - Audio Engineering Society
LA  - en
AB  - The aim of the here developed new taxonomies is to describe the components involved in the quality process of Auditory Virtual Environments (AVE), and to quantify the relations between them for different applications. The taxonomy should allow an overview and identify the relations which are most important in the software development process and the design of listening experiments. For the first time the multivariate relations between the Quality Elements in the physical domain and the Quality Features in the perceptual domain of such a Quality Taxonomy are evaluated for three different AVE applications. This evaluation and quantification is done by means of an expert survey (DELPHI method) to objectify the results. Principal Component Analysis reveals that five dimensions are necessary to describe about 95% of the variance in the data. This indicates that the selected seven Quality Features are clearly distinguishable for the experts, but not orthogonal to each other. Most of the Quality Features are introduced meaningful terms in the audio engineering field and therefore usable without training for the participating experts. The results of the expert survey are compared to listening test results, which use the same Quality Features. The bottom line is that the expert survey is not only a much faster method to get a good overview about a specific application as compared to the listening test, but it also reveals more information about it.
ER  -