Binaural Processing Algorithms: Importance of Clustering Analysis for Preference Tests

Silzle A, Neugebauer B, George S, Plogsties J. Binaural Processing Algorithms: Importance of Clustering Analysis for Preference Tests. In: AES Convention 126. Audio Engineering Society; 2009. Paper 7728. Available from: https://aes.org/publications/elibrary-page/?id=14924

@inproceedings{Silzle2009_14924,
  author = {Silzle, Andreas and Neugebauer, Bernhard and George, Sunish and Plogsties, Jan},
  title = {{Binaural Processing Algorithms: Importance of Clustering Analysis for Preference Tests}},
  booktitle = {AES Convention 126},
  note = {Paper 7728},
  year = {2009},
  month = may,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=14924}
}

TY  - CPAPER
TI  - Binaural Processing Algorithms: Importance of Clustering Analysis for Preference Tests
AU  - Silzle, Andreas
AU  - Neugebauer, Bernhard
AU  - George, Sunish
AU  - Plogsties, Jan
T2  - AES Convention 126
M1  - Paper 7728
PY  - 2009
DA  - 2009/05/06
UR  - https://aes.org/publications/elibrary-page/?id=14924
PB  - Audio Engineering Society
LA  - en
AB  - The acceptability of a newly proposed technology for commercial application is often assumed if the sound quality reached in a listening test surpasses a certain target threshold. As an example, it is a well-established procedure for decisions on the deployment of audio codecs to run a listening test comparing the coded/decoded signal with the uncoded reference signal. For other technologies, e.g. upmix or binaural processing, however, the unprocessed signal only can act as a “comparison signal”. Here, the goal is to achieve a significant preference of the processed over the comparison signal. For such preference listening tests, we underline the importance of clustering the test results to obtain additional valuable information, as opposed to using the standard statistic metrics like mean and confidence interval. This approach allows determining the size of the user group that significantly prefers to use the proposed algorithm when it would be available in a consumer device. As an example, listening test data for binaural processing algorithms are analyzed in this investigation.
ER  -