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S. Olive, T. Welti, and O. Khonsaripour, "A Statistical Model that Predicts Listeners’ Preference Ratings of In-Ear Headphones: Part 1—Listening Test Results and Acoustic Measurements," Paper 9840, (2017 October.). doi: S. Olive, T. Welti, and O. Khonsaripour, "A Statistical Model that Predicts Listeners’ Preference Ratings of In-Ear Headphones: Part 1—Listening Test Results and Acoustic Measurements," Paper 9840, (2017 October.). doi:
Abstract: A series of controlled listening tests were conducted on 30 different models of in-ear (IE) headphones to measure their relative sound quality. A total of 71 listeners both trained and untrained rated the headphones on a 100-point preference scale using a multiple stimulus method with a hidden reference and low anchor. A virtual headphone test method was used wherein each headphone was simulated over a high-quality replicator headphone equalized to match their measured magnitude response. Leakage was monitored and eliminated for each subject. The results revealed both trained and untrained listeners preferred the hidden reference, which was the replicator headphone equalized to our new IE headphone target response curve. The further the other headphones deviated from the target response, the less they were preferred. Part two of this paper develops a statistical model that predicts the headphone preference ratings based on their acoustic measurements.

@article{olive2017a,
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 in-ear headphones: part 1—listening test results and acoustic measurements},
year={2017},
volume={},
number={},
pages={},
doi={},
month={october},} @article{olive2017a,
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 in-ear headphones: part 1—listening test results and acoustic measurements},
year={2017},
volume={},
number={},
pages={},
doi={},
month={october},
abstract={a series of controlled listening tests were conducted on 30 different models of in-ear (ie) headphones to measure their relative sound quality. a total of 71 listeners both trained and untrained rated the headphones on a 100-point preference scale using a multiple stimulus method with a hidden reference and low anchor. a virtual headphone test method was used wherein each headphone was simulated over a high-quality replicator headphone equalized to match their measured magnitude response. leakage was monitored and eliminated for each subject. the results revealed both trained and untrained listeners preferred the hidden reference, which was the replicator headphone equalized to our new ie headphone target response curve. the further the other headphones deviated from the target response, the less they were preferred. part two of this paper develops a statistical model that predicts the headphone preference ratings based on their acoustic measurements.},}

TY - paper
TI - A Statistical Model that Predicts Listeners’ Preference Ratings of In-Ear Headphones: Part 1—Listening Test Results and Acoustic Measurements
SP - EP -
AU - Olive, Sean
AU - Welti, Todd
AU - Khonsaripour, Omid
PY - 2017
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - October 2017 TY - paper
TI - A Statistical Model that Predicts Listeners’ Preference Ratings of In-Ear Headphones: Part 1—Listening Test Results and Acoustic Measurements
SP - EP -
AU - Olive, Sean
AU - Welti, Todd
AU - Khonsaripour, Omid
PY - 2017
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - October 2017
AB - A series of controlled listening tests were conducted on 30 different models of in-ear (IE) headphones to measure their relative sound quality. A total of 71 listeners both trained and untrained rated the headphones on a 100-point preference scale using a multiple stimulus method with a hidden reference and low anchor. A virtual headphone test method was used wherein each headphone was simulated over a high-quality replicator headphone equalized to match their measured magnitude response. Leakage was monitored and eliminated for each subject. The results revealed both trained and untrained listeners preferred the hidden reference, which was the replicator headphone equalized to our new IE headphone target response curve. The further the other headphones deviated from the target response, the less they were preferred. Part two of this paper develops a statistical model that predicts the headphone preference ratings based on their acoustic measurements.