Automatic Statistical Analysis of Ear Shape Anthropometrics of an International Ear Shape Database

Kaneko S, Suenaga T, Okumura H, Kim S. Automatic Statistical Analysis of Ear Shape Anthropometrics of an International Ear Shape Database. In: AES Conference: 2019 AES International Conference on Immersive and Interactive Audio. Audio Engineering Society; 2019. Paper 8. Available from: https://aes.org/publications/elibrary-page/?id=20405

@inproceedings{Kaneko2019_20405,
  author = {Kaneko, Shoken and Suenaga, Tsukasa and Okumura, Hiraku and Kim, Sungyoung},
  title = {{Automatic Statistical Analysis of Ear Shape Anthropometrics of an International Ear Shape Database}},
  booktitle = {AES Conference: 2019 AES International Conference on Immersive and Interactive Audio},
  note = {Paper 8},
  year = {2019},
  month = mar,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=20405}
}

TY  - CPAPER
TI  - Automatic Statistical Analysis of Ear Shape Anthropometrics of an International Ear Shape Database
AU  - Kaneko, Shoken
AU  - Suenaga, Tsukasa
AU  - Okumura, Hiraku
AU  - Kim, Sungyoung
T2  - AES Conference: 2019 AES International Conference on Immersive and Interactive Audio
M1  - Paper 8
PY  - 2019
DA  - 2019/03/06
UR  - https://aes.org/publications/elibrary-page/?id=20405
PB  - Audio Engineering Society
LA  - en
AB  - We present a statistical analysis result of ear-related anthropometric data measured from 162 subjects and its subsets divided by gender and race. To analyze the data efficiently, we have developed a measurement technique that is semi-automatic, and therefore, can scale to larger data sets. The results show that, ear dimensions of Asian subjects’ ears tend to be larger than those of non-Asian subjects. Statistical tests confirmed the significant difference of the ear dimensions between different gender and racial categories. These findings suggest the importance of taking into account the subject’s demographic information such as gender or race, for generalized or individualized HRTF data based on ear shape modeling for immersive audio applications.
ER  -