Comparison of Distortion Products in Headphone Equalization Algorithms for Binaural Synthesis

Boren B, Geronazzo M. Comparison of Distortion Products in Headphone Equalization Algorithms for Binaural Synthesis. In: AES Conference: 2020 AES International Conference on Audio for Virtual and Augmented Reality (August 2020). Audio Engineering Society; 2020. Paper 10501. Available from: https://aes.org/publications/elibrary-page/?id=21178

@inproceedings{Boren2020_21178,
  author = {Boren, Braxton and Geronazzo, Michele},
  title = {{Comparison of Distortion Products in Headphone Equalization Algorithms for Binaural Synthesis}},
  booktitle = {AES Conference: 2020 AES International Conference on Audio for Virtual and Augmented Reality (August 2020)},
  note = {Paper 10501},
  year = {2020},
  month = aug,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=21178}
}

TY  - CPAPER
TI  - Comparison of Distortion Products in Headphone Equalization Algorithms for Binaural Synthesis
AU  - Boren, Braxton
AU  - Geronazzo, Michele
T2  - AES Conference: 2020 AES International Conference on Audio for Virtual and Augmented Reality (August 2020)
M1  - Paper 10501
PY  - 2020
DA  - 2020/08/06
UR  - https://aes.org/publications/elibrary-page/?id=21178
PB  - Audio Engineering Society
LA  - en
AB  - Headphone design has traditionally focused on creating a frequency response to make commercial stereo audio sound more natural. However, because of the sensitivity of spatial hearing to frequency-dependent cues, binaural reproduction requires headphones’ target spectrum to be as flat as possible. Initial attempts to equalize headphones used a naive inversion of the headphone spectrum, which degraded binaural content because the headphone transfer function (HpTF) changes each time headphones are re-seated. Many different algorithms have been proposed to improve binaural equalization, each of which has been tested over a limited sample of HpTFs. The present study gathered 1550 HpTFs from different institutions into a single dataset for large-scale comparisons of equalization algorithms. A numerical metric was designed to quantify auditory perception of spectral coloration from ’ringing’ peaks in the post-equalization HpTF. Using this metric, eight of the most prominent equalization methods have been compared over the aggregate HpTF dataset. High-shelf regularization is shown to outperform all other equalization techniques using either individualized or averaged input spectra. In addition, high-shelf regularization without individual measurements gives less average coloration than direct inversion using individualized equalization.
ER  -