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R. Ranjan, and W. Gan, "Adaptive Equalization of Natural Augmented Reality Headset Using Non-Stationary Virtual Signals," Paper 2-5, (2016 August.). doi: R. Ranjan, and W. Gan, "Adaptive Equalization of Natural Augmented Reality Headset Using Non-Stationary Virtual Signals," Paper 2-5, (2016 August.). doi:
Abstract: 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.

@article{ranjan2016adaptive,
author={ranjan, rishabh and gan, woon-seng},
journal={journal of the audio engineering society},
title={adaptive equalization of natural augmented reality headset using non-stationary virtual signals},
year={2016},
volume={},
number={},
pages={},
doi={},
month={august},} @article{ranjan2016adaptive,
author={ranjan, rishabh and gan, woon-seng},
journal={journal of the audio engineering society},
title={adaptive equalization of natural augmented reality headset using non-stationary virtual signals},
year={2016},
volume={},
number={},
pages={},
doi={},
month={august},
abstract={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.},}

TY - paper
TI - Adaptive Equalization of Natural Augmented Reality Headset Using Non-Stationary Virtual Signals
SP - EP -
AU - Ranjan, Rishabh
AU - Gan, Woon-Seng
PY - 2016
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - August 2016 TY - paper
TI - Adaptive Equalization of Natural Augmented Reality Headset Using Non-Stationary Virtual Signals
SP - EP -
AU - Ranjan, Rishabh
AU - Gan, Woon-Seng
PY - 2016
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - August 2016
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.