QESTRAL (Part 1): Quality Evaluation of Spatial Transmission and Reproduction Using an Artificial Listener

Rumsey F, Zielinski S, Jackson P, Dewhirst M, Conetta R, George S, Bech S, Meares D. QESTRAL (Part 1): Quality Evaluation of Spatial Transmission and Reproduction Using an Artificial Listener. In: AES Convention 125. Audio Engineering Society; 2008. Paper 7595. Available from: https://aes.org/publications/elibrary-page/?id=14746

@inproceedings{Rumsey2008_14746,
  author = {Rumsey, Francis and Zielinski, Slawomir and Jackson, Philip and Dewhirst, Martin and Conetta, Robert and George, Sunish and Bech, Søren and Meares, David},
  title = {{QESTRAL (Part 1): Quality Evaluation of Spatial Transmission and Reproduction Using an Artificial Listener}},
  booktitle = {AES Convention 125},
  note = {Paper 7595},
  year = {2008},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=14746}
}

TY  - CPAPER
TI  - QESTRAL (Part 1): Quality Evaluation of Spatial Transmission and Reproduction Using an Artificial Listener
AU  - Rumsey, Francis
AU  - Zielinski, Slawomir
AU  - Jackson, Philip
AU  - Dewhirst, Martin
AU  - Conetta, Robert
AU  - George, Sunish
AU  - Bech, Søren
AU  - Meares, David
T2  - AES Convention 125
M1  - Paper 7595
PY  - 2008
DA  - 2008/10/06
UR  - https://aes.org/publications/elibrary-page/?id=14746
PB  - Audio Engineering Society
LA  - en
AB  - Most current perceptual models for audio quality have so far tended to concentrate on the audibility of distortions and noises that mainly affect the timbre of reproduced sound. The QESTRAL model, however, is specifically designed to take account of distortions in the spatial domain such as changes in source location, width and envelopment. It is not aimed only at codec quality evaluation but at a wider range of spatial distortions that can arise in audio processing and reproduction systems. The model has been calibrated against a large database of listening tests designed to evaluate typical audio processes, comparing spatially degraded multichannel audio material against a reference. Using a range of relevant metrics and a sophisticated multivariate regression model, results are obtained that closely match those obtained in listening tests.
ER  -