Use of the Magnitude Estimation Technique in Reference-Free Assessments of Spatial Audio Technology

Brandmeyer A, Darcy D, Lu L, Graff R, Swedlow N, Crum P. Use of the Magnitude Estimation Technique in Reference-Free Assessments of Spatial Audio Technology. In: AES Convention 147. Audio Engineering Society; 2019. Paper 10273. Available from: https://aes.org/publications/elibrary-page/?id=20646

@inproceedings{Brandmeyer2019_20646,
  author = {Brandmeyer, Alex and Darcy, Dan and Lu, Lie and Graff, Richard and Swedlow, Nathan and Crum, Poppy},
  title = {{Use of the Magnitude Estimation Technique in Reference-Free Assessments of Spatial Audio Technology}},
  booktitle = {AES Convention 147},
  note = {Paper 10273},
  year = {2019},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=20646}
}

TY  - CPAPER
TI  - Use of the Magnitude Estimation Technique in Reference-Free Assessments of Spatial Audio Technology
AU  - Brandmeyer, Alex
AU  - Darcy, Dan
AU  - Lu, Lie
AU  - Graff, Richard
AU  - Swedlow, Nathan
AU  - Crum, Poppy
T2  - AES Convention 147
M1  - Paper 10273
PY  - 2019
DA  - 2019/10/06
UR  - https://aes.org/publications/elibrary-page/?id=20646
PB  - Audio Engineering Society
LA  - en
AB  - Magnitude estimation is a technique developed in psychophysics research in which participants numerically estimate the relative strengths of a sequence of stimuli along a relevant dimension. Traditionally, the method has been used to measure basic perceptual phenomena in different sensory modalities (e.g., "brightness," "loudness"). We present two examples of using magnitude estimation in the domain of audio rendering for different categories of consumer electronics devices. Importantly, magnitude estimation doesn’t require a reference stimulus and can be used to assess general ("audio quality") and domain-specific (e.g., "spaciousness") attributes. Additionally, we show how this data can be used together with objective measurements of the tested systems in a model that can predict performance of systems not included in the original assessment.
ER  -