On Some Biases Encountered in Modern Audio Quality Listening Tests (Part 2): Selected Graphical Examples and Discussion

Zielinski S. On Some Biases Encountered in Modern Audio Quality Listening Tests (Part 2): Selected Graphical Examples and Discussion. J Audio Eng Soc. 2016;64(1/2):55-74. doi:10.17743/jaes.2015.0094

@article{Zielinski2016_18105,
  author = {Zielinski, Slawomir},
  title = {{On Some Biases Encountered in Modern Audio Quality Listening Tests (Part 2): Selected Graphical Examples and Discussion}},
  journal = {Journal of the Audio Engineering Society},
  volume = {64},
  number = {1/2},
  pages = {55--74},
  year = {2016},
  month = jan,
  publisher = {Audio Engineering Society},
  doi = {10.17743/jaes.2015.0094},
  url = {https://doi.org/10.17743/jaes.2015.0094}
}

TY  - JOUR
TI  - On Some Biases Encountered in Modern Audio Quality Listening Tests (Part 2): Selected Graphical Examples and Discussion
AU  - Zielinski, Slawomir
T2  - Journal of the Audio Engineering Society
J2  - J. Audio Eng. Soc.
VL  - 64
IS  - 1/2
SP  - 55
EP  - 74
PY  - 2016
DA  - 2016/01/06
DO  - 10.17743/jaes.2015.0094
UR  - https://doi.org/10.17743/jaes.2015.0094
PB  - Audio Engineering Society
LA  - en
AB  - Measuring audio quality is particularly difficult because the measurement methodology itself strongly biases the results. While a previous paper by the same author covered a broad range of biases, this report focuses only on five types of systemic error potentially affecting quantifying judgments: range equalization bias, stimulus spacing bias, contradiction bias, and biases due to nonlinear properties of the assessment scale. These biases are prevalent in audio and speech quality evaluations. Empirical data obtained by various researchers over the past fifteen years was used to illustrate biases in a graphical representation. The results conclusively show that assessment methods are inherently relative. These results also raise important questions about the utility of verbal descriptors. Researchers should avoid conclusions about quality by associating numerical scores with verbal descriptors at fixed positions along the scale.
ER  -