An Unintrusive Objective Model for Predicting the Sensation of Envelopment Arising from Surround Sound Recordings

George S, Zielinski S, Rumsey F, Conetta R, Dewhirst M, Jackson P, Meares D, Bech S. An Unintrusive Objective Model for Predicting the Sensation of Envelopment Arising from Surround Sound Recordings. In: AES Convention 125. Audio Engineering Society; 2008. Paper 7599. Available from: https://aes.org/publications/elibrary-page/?id=14750

@inproceedings{George2008_14750,
  author = {George, Sunish and Zielinski, Slawomir and Rumsey, Francis and Conetta, Robert and Dewhirst, Martin and Jackson, Philip and Meares, David and Bech, Søren},
  title = {{An Unintrusive Objective Model for Predicting the Sensation of Envelopment Arising from Surround Sound Recordings}},
  booktitle = {AES Convention 125},
  note = {Paper 7599},
  year = {2008},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=14750}
}

TY  - CPAPER
TI  - An Unintrusive Objective Model for Predicting the Sensation of Envelopment Arising from Surround Sound Recordings
AU  - George, Sunish
AU  - Zielinski, Slawomir
AU  - Rumsey, Francis
AU  - Conetta, Robert
AU  - Dewhirst, Martin
AU  - Jackson, Philip
AU  - Meares, David
AU  - Bech, Søren
T2  - AES Convention 125
M1  - Paper 7599
PY  - 2008
DA  - 2008/10/06
UR  - https://aes.org/publications/elibrary-page/?id=14750
PB  - Audio Engineering Society
LA  - en
AB  - This paper describes the development of an unintrusive objective model, developed independently as a part of the QESTRAL project, for predicting the sensation of envelopment arising from commercially available 5-channel surround sound recordings. The model was calibrated using subjective scores obtained from listening tests that used a grading scale defined by audible anchors. For predicting subjective scores, a number of features based on Inter-aural Cross Correlation (IACC), Karhunen-Loeve Transform (KLT) and signal energy levels were extracted from recordings. The ridge regression technique was used to build the objective model and a calibrated model was validated using a listening test scores database obtained from a different group of listeners, stimuli and location. The initial results showed a high correlation between predicted and actual scores obtained from the listening tests.
ER  -