S. B. Chon and K.-M. Sung, “Sound Quality Assessment of Earphone: A Subjective Assessment Procedure and an Objective Prediction Model,” in Proc. AES Conference: 38th International Conference: Sound Quality Evaluation, Jun. 2010, Paper 8-4. [Online]. Available: https://aes.org/publications/elibrary-page/?id=15475
Chon SB, Sung KM. Sound Quality Assessment of Earphone: A Subjective Assessment Procedure and an Objective Prediction Model. In: AES Conference: 38th International Conference: Sound Quality Evaluation. Audio Engineering Society; 2010. Paper 8-4. Available from: https://aes.org/publications/elibrary-page/?id=15475
@inproceedings{Chon2010_15475,
author = {Chon, Sang Bae and Sung, Koeng-Mo},
title = {{Sound Quality Assessment of Earphone: A Subjective Assessment Procedure and an Objective Prediction Model}},
booktitle = {AES Conference: 38th International Conference: Sound Quality Evaluation},
note = {Paper 8-4},
year = {2010},
month = jun,
publisher = {Audio Engineering Society},
url = {https://aes.org/publications/elibrary-page/?id=15475}
}
TY - CPAPER
TI - Sound Quality Assessment of Earphone: A Subjective Assessment Procedure and an Objective Prediction Model
AU - Chon, Sang Bae
AU - Sung, Koeng-Mo
T2 - AES Conference: 38th International Conference: Sound Quality Evaluation
M1 - Paper 8-4
PY - 2010
DA - 2010/06/06
UR - https://aes.org/publications/elibrary-page/?id=15475
PB - Audio Engineering Society
LA - en
AB - This paper describes a subjective assessment procedure and an objective prediction model for sound quality assessment of earphones. The proposed subjective assessment procedure is designed to provide an instantaneous switching from one stimulus to another or reference stimulus. Based on the result from subjective assessment, an objective prediction model was designed using Average Distorted Block (ADBB) [1]. Eight earphones were assessed by the proposed subjective assessment procedure. The perceived Mean Opinion Score (MOSs) were used in the regression process in ADBB. The result shows that the proposed objective prediction model achieves an outstanding performance with a cross correlation of 0.9 and root mean square error of 7.57 in a 100 point scale.
ER -