Knowledge Distillation-Based Personalized HRTF Estimation Toward Real World

Lee GW, Kim HK, Chun CJ. Knowledge Distillation-Based Personalized HRTF Estimation Toward Real World. In: AES Convention 155. Audio Engineering Society; 2023. Paper 116. Available from: https://aes.org/publications/elibrary-page/?id=22270

@inproceedings{Lee2023_22270,
  author = {Lee, Geon Woo and Kim, Hong Kook and Chun, Chan Jun},
  title = {{Knowledge Distillation-Based Personalized HRTF Estimation Toward Real World}},
  booktitle = {AES Convention 155},
  note = {Paper 116},
  year = {2023},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=22270}
}

TY  - CPAPER
TI  - Knowledge Distillation-Based Personalized HRTF Estimation Toward Real World
AU  - Lee, Geon Woo
AU  - Kim, Hong Kook
AU  - Chun, Chan Jun
T2  - AES Convention 155
M1  - Paper 116
PY  - 2023
DA  - 2023/10/06
UR  - https://aes.org/publications/elibrary-page/?id=22270
PB  - Audio Engineering Society
LA  - en
AB  - This paper proposes a new personalized head-related transfer function (HRTF) estimation method based on knowledge distillation (KD). The KD pipeline for generating personalized HRTFs comprises a teacher–student model for transferring well-trained knowledge. The teacher model is the expert that generates personalized HRTFs by representing extensive knowledge using all anthropometric data and ear image. In contrast, the student model is the mimic that attempts to learn from the expert using seven anthropometric data and ear image. The performance of the proposed personalized HRTF estimation approach is evaluated using the Center for Image Processing and Integrated Computing (CIPIC) database. The experiments reveal that the proposed method showed equivalent performance for the root mean squared error and log spectral distance measurements compared to the method using all anthropometric data with the ear images.
ER  -