Adaptive Equalization of Natural Augmented Reality Headset Using Non-Stationary Virtual Signals

Ranjan R, Gan WS. Adaptive Equalization of Natural Augmented Reality Headset Using Non-Stationary Virtual Signals. In: AES Conference: 2016 AES International Conference on Headphone Technology. Audio Engineering Society; 2016. Paper 2-5. Available from: https://aes.org/publications/elibrary-page/?id=18344

@inproceedings{Ranjan2016_18344,
  author = {Ranjan, Rishabh and Gan, Woon-Seng},
  title = {{Adaptive Equalization of Natural Augmented Reality Headset Using Non-Stationary Virtual Signals}},
  booktitle = {AES Conference: 2016 AES International Conference on Headphone Technology},
  note = {Paper 2-5},
  year = {2016},
  month = aug,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=18344}
}

TY  - CPAPER
TI  - Adaptive Equalization of Natural Augmented Reality Headset Using Non-Stationary Virtual Signals
AU  - Ranjan, Rishabh
AU  - Gan, Woon-Seng
T2  - AES Conference: 2016 AES International Conference on Headphone Technology
M1  - Paper 2-5
PY  - 2016
DA  - 2016/08/06
UR  - https://aes.org/publications/elibrary-page/?id=18344
PB  - Audio Engineering Society
LA  - en
AB  - A natural integration of virtual sound sources with the real environment soundscape using a natural augmented reality (NAR) headset is discussed in this paper. These NAR headsets consist of dual sensing microphones at each earcup and employ adaptive filtering technique to achieve natural listening in augmented reality applications. We propose an adaptive equalization of the open-back NAR headsets using non-stationary virtual signals to compensate for individualized headphones transfer function (HPTF) and acoustic coupling to seamlessly mix virtual sound with the environmental sound. Training of the NAR headsets are carried out using fast-converging normalized filtered-x least mean square algorithms to respond to changing sound variation. Significant changes in HPTF can be detected online and fast HPTF estimation using normalized least mean square algorithm is employed to update the secondary path estimates.
ER  -