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S. Olive, T. Welti, and O. Khonsaripour, "A Statistical Model that Predicts Listeners’ Preference Ratings of Around-Ear and On-Ear Headphones," Paper 9919, (2018 May.). doi: S. Olive, T. Welti, and O. Khonsaripour, "A Statistical Model that Predicts Listeners’ Preference Ratings of Around-Ear and On-Ear Headphones," Paper 9919, (2018 May.). doi:
Abstract: A controlled listening test was conducted on 31 different models of around-ear (AE) and on-ear (OE) headphones to determine listeners’ sound quality preferences. One-hundred-thirty listeners both trained and untrained rated the headphones based on preference using a virtual headphone method that used a single replicator headphone equalized to match magnitude and minimum phase responses of the different headphones. Listeners rated seven different headphones in each trial that included high (the new Harman AE-OE target curve) and low anchors. On average, both trained and untrained listeners preferred the high anchor to 31 other choices. Using machine learning a model was developed that predicts the listeners’ headphone preference ratings of the headphones based on deviation in magnitude response from the Harman target curve.

@article{olive2018a,
author={olive, sean and welti, todd and khonsaripour, omid},
journal={journal of the audio engineering society},
title={a statistical model that predicts listeners’ preference ratings of around-ear and on-ear headphones},
year={2018},
volume={},
number={},
pages={},
doi={},
month={may},} @article{olive2018a,
author={olive, sean and welti, todd and khonsaripour, omid},
journal={journal of the audio engineering society},
title={a statistical model that predicts listeners’ preference ratings of around-ear and on-ear headphones},
year={2018},
volume={},
number={},
pages={},
doi={},
month={may},
abstract={a controlled listening test was conducted on 31 different models of around-ear (ae) and on-ear (oe) headphones to determine listeners’ sound quality preferences. one-hundred-thirty listeners both trained and untrained rated the headphones based on preference using a virtual headphone method that used a single replicator headphone equalized to match magnitude and minimum phase responses of the different headphones. listeners rated seven different headphones in each trial that included high (the new harman ae-oe target curve) and low anchors. on average, both trained and untrained listeners preferred the high anchor to 31 other choices. using machine learning a model was developed that predicts the listeners’ headphone preference ratings of the headphones based on deviation in magnitude response from the harman target curve.},}

TY - paper
TI - A Statistical Model that Predicts Listeners’ Preference Ratings of Around-Ear and On-Ear Headphones
SP - EP -
AU - Olive, Sean
AU - Welti, Todd
AU - Khonsaripour, Omid
PY - 2018
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - May 2018 TY - paper
TI - A Statistical Model that Predicts Listeners’ Preference Ratings of Around-Ear and On-Ear Headphones
SP - EP -
AU - Olive, Sean
AU - Welti, Todd
AU - Khonsaripour, Omid
PY - 2018
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - May 2018
AB - A controlled listening test was conducted on 31 different models of around-ear (AE) and on-ear (OE) headphones to determine listeners’ sound quality preferences. One-hundred-thirty listeners both trained and untrained rated the headphones based on preference using a virtual headphone method that used a single replicator headphone equalized to match magnitude and minimum phase responses of the different headphones. Listeners rated seven different headphones in each trial that included high (the new Harman AE-OE target curve) and low anchors. On average, both trained and untrained listeners preferred the high anchor to 31 other choices. Using machine learning a model was developed that predicts the listeners’ headphone preference ratings of the headphones based on deviation in magnitude response from the Harman target curve.