Measurements to Determine the Ranking Accuracy of Perceptual Models

Pearce A, Brookes T, Mason R, Dewhirst M. Measurements to Determine the Ranking Accuracy of Perceptual Models. In: AES Convention 140. Audio Engineering Society; 2016. Paper 9515. Available from: https://aes.org/publications/elibrary-page/?id=18214

@inproceedings{Pearce2016_18214,
  author = {Pearce, Andy and Brookes, Tim and Mason, Russell and Dewhirst, Martin},
  title = {{Measurements to Determine the Ranking Accuracy of Perceptual Models}},
  booktitle = {AES Convention 140},
  note = {Paper 9515},
  year = {2016},
  month = may,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=18214}
}

TY  - CPAPER
TI  - Measurements to Determine the Ranking Accuracy of Perceptual Models
AU  - Pearce, Andy
AU  - Brookes, Tim
AU  - Mason, Russell
AU  - Dewhirst, Martin
T2  - AES Convention 140
M1  - Paper 9515
PY  - 2016
DA  - 2016/05/06
UR  - https://aes.org/publications/elibrary-page/?id=18214
PB  - Audio Engineering Society
LA  - en
AB  - Linear regression is commonly used in the audio industry to create objective measurement models that predict subjective data. For any model development, the measure used to evaluate the accuracy of the prediction is important. The most common of these assume a linear relationship between the subjective data and the prediction, though in the early stages of model development this is not always the case. Measures based on rank ordering (such as Spearman’s test), can alternatively be used. Spearman’s test, however, does not consider the variance of the subjective results. This paper presents a method of incorporating the subjective variance in the Spearman’s rank ordering test using Monte Carlo simulations and shows how this can be used to develop predictive models.
ER  -