Analysis of Subjective Data from the MPEG Unified Speech and Audio Coding Call for Proposals

Quackenbush S, Gross AM. Analysis of Subjective Data from the MPEG Unified Speech and Audio Coding Call for Proposals. In: AES Conference: 38th International Conference: Sound Quality Evaluation. Audio Engineering Society; 2010. Paper 7-3. Available from: https://aes.org/publications/elibrary-page/?id=15483

@inproceedings{Quackenbush2010_15483,
  author = {Quackenbush, Schuyler and Gross, Alan M.},
  title = {{Analysis of Subjective Data from the MPEG Unified Speech and Audio Coding Call for Proposals}},
  booktitle = {AES Conference: 38th International Conference: Sound Quality Evaluation},
  note = {Paper 7-3},
  year = {2010},
  month = jun,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=15483}
}

TY  - CPAPER
TI  - Analysis of Subjective Data from the MPEG Unified Speech and Audio Coding Call for Proposals
AU  - Quackenbush, Schuyler
AU  - Gross, Alan M.
T2  - AES Conference: 38th International Conference: Sound Quality Evaluation
M1  - Paper 7-3
PY  - 2010
DA  - 2010/06/06
UR  - https://aes.org/publications/elibrary-page/?id=15483
PB  - Audio Engineering Society
LA  - en
AB  - In October 2007 ISO/IEC MPEG issued a Call for Unified Speech and Audio Coding. Eleven technologies were proposed and each was evaluated at nine operating points (i.e. bit rate for either mono or stereo signals) using the MUSHRA test methodology. The test was conducted at seven sites comprising a total of 64,500 individual subjective scores. The paper contrasts a simple 95% confidence interval on the mean scores and an analysis of variance (ANOVA) model for the set of data. A deeper analysis validated the assumption of Gaussian distribution for the model errors and the ANOVA methodology gave more that 25% greater power than the simple grand mean analysis. In other words, the ANOVA reduced the 95% confidence interval on the grand mean by 25% or, conversely, would have permitted fewer listeners in the testing effort in order to get the same results as the simple grand mean analysis.
ER  -