J. Salmela and V.-V. Mattila, “New Intrusive Method for the Objective Quality Evaluation of Acoustic Noise Suppression in Mobile Communications,” in Proc. AES Convention 116, May 2004, Paper 6145. [Online]. Available: https://aes.org/publications/elibrary-page/?id=12628
Salmela J, Mattila VV. New Intrusive Method for the Objective Quality Evaluation of Acoustic Noise Suppression in Mobile Communications. In: AES Convention 116. Audio Engineering Society; 2004. Paper 6145. Available from: https://aes.org/publications/elibrary-page/?id=12628
@inproceedings{Salmela2004_12628,
author = {Salmela, Juha and Mattila, Ville-Veikko},
title = {{New Intrusive Method for the Objective Quality Evaluation of Acoustic Noise Suppression in Mobile Communications}},
booktitle = {AES Convention 116},
note = {Paper 6145},
year = {2004},
month = may,
publisher = {Audio Engineering Society},
url = {https://aes.org/publications/elibrary-page/?id=12628}
}
TY - CPAPER
TI - New Intrusive Method for the Objective Quality Evaluation of Acoustic Noise Suppression in Mobile Communications
AU - Salmela, Juha
AU - Mattila, Ville-Veikko
T2 - AES Convention 116
M1 - Paper 6145
PY - 2004
DA - 2004/05/06
UR - https://aes.org/publications/elibrary-page/?id=12628
PB - Audio Engineering Society
LA - en
AB - A new intrusive method, combined of several independent objective metrics, has been developed for the evaluation of the quality of acoustic noise suppression in mobile communications. Extensive subjective data, including simulations of several noise suppression solutions in various noise environments, was gathered to serve as the benchmark for the metrics. Partial least-square regression and full cross-validation were used to establish the applicability of 26 metrics, that were making use of different measurement procedures, to predict the perceived quality. A Phase IV, vector-based preference model, was optimized to predict quality with a correlation of 0.95, resulting in an average prediction error of 8 %. Different measurement procedures appeared to contribute with a similar extent to the prediction ability of the optimized model.
ER -