Predicting Perceived Off-Center Sound Degradation in Surround Loudspeaker Setups for Various Multichannel Microphone Techniques

Peters N, Giordano BL, Kim S, Braasch J, McAdams S. Predicting Perceived Off-Center Sound Degradation in Surround Loudspeaker Setups for Various Multichannel Microphone Techniques. In: AES Convention 125. Audio Engineering Society; 2008. Paper 7580. Available from: https://aes.org/publications/elibrary-page/?id=14732

@inproceedings{Peters2008_14732,
  author = {Peters, Nils and Giordano, Bruno L. and Kim, Sungyoung and Braasch, Jonas and McAdams, Stephen},
  title = {{Predicting Perceived Off-Center Sound Degradation in Surround Loudspeaker Setups for Various Multichannel Microphone Techniques}},
  booktitle = {AES Convention 125},
  note = {Paper 7580},
  year = {2008},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=14732}
}

TY  - CPAPER
TI  - Predicting Perceived Off-Center Sound Degradation in Surround Loudspeaker Setups for Various Multichannel Microphone Techniques
AU  - Peters, Nils
AU  - Giordano, Bruno L.
AU  - Kim, Sungyoung
AU  - Braasch, Jonas
AU  - McAdams, Stephen
T2  - AES Convention 125
M1  - Paper 7580
PY  - 2008
DA  - 2008/10/06
UR  - https://aes.org/publications/elibrary-page/?id=14732
PB  - Audio Engineering Society
LA  - en
AB  - Multiple listening tests were conducted to examine the influence of microphone techniques on the quality of sound reproduction. Generally, testing focuses on the central listening position (CLP), and neglects off-center listening positions. Exploratory tests focusing on the degradation in sound quality at off-center listening positions were presented at the 123rd AES Convention. Results showed that the recording technique does influence the degree of sound degradation at off-center positions. This paper focuses on the analysis of the binaural re-recording at the different listening positions in order to interpret the results of the previous listening tests. Multiple linear regression is used to create a predictive model which accounts for 85% of the variance in the behavioral data. The primary successful predictors were spectral and the secondary predictors were spatial in nature.
ER  -