Overview of speech quality metrics in terms of automated evaluation of signal denoising in a presence of non-stationary noise
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K. Duzinkiewicz, D. Koszewski, K. Pietrusinska, and P. Trella, "Overview of speech quality metrics in terms of automated evaluation of signal denoising in a presence of non-stationary noise," Engineering Brief 625, (2020 October.). doi:
K. Duzinkiewicz, D. Koszewski, K. Pietrusinska, and P. Trella, "Overview of speech quality metrics in terms of automated evaluation of signal denoising in a presence of non-stationary noise," Engineering Brief 625, (2020 October.). doi:
Abstract: Recent developments in neural network-based speech enhancement ask for a robust subjective metric that can be used for comparing performance of a different noise suppression algorithms (for non-stationary noises), that would closely match costly and time-consuming subjective user tests. The article describes results of a comparison between subjective scores obtained using MUSHRA methodology vs. automated evaluation with objective metrics i.e. POLQA, 3QUEST, STOI & ESTOI, on a set of recordings processed by 2 different denoising algorithms for close & far speaker distance. Correlation coefficient is calculated between subjective scores and examined metrics. The results are based on recordings simulated using an in-house simulation toolchain, based on impulse responses from actual laptop device used in low reverb quiet room.
@article{duzinkiewicz2020overview,
author={duzinkiewicz, karol and koszewski, damian and pietrusinska, kamila and trella, pawel},
journal={journal of the audio engineering society},
title={overview of speech quality metrics in terms of automated evaluation of signal denoising in a presence of non-stationary noise},
year={2020},
volume={},
number={},
pages={},
doi={},
month={october},}
@article{duzinkiewicz2020overview,
author={duzinkiewicz, karol and koszewski, damian and pietrusinska, kamila and trella, pawel},
journal={journal of the audio engineering society},
title={overview of speech quality metrics in terms of automated evaluation of signal denoising in a presence of non-stationary noise},
year={2020},
volume={},
number={},
pages={},
doi={},
month={october},
abstract={recent developments in neural network-based speech enhancement ask for a robust subjective metric that can be used for comparing performance of a different noise suppression algorithms (for non-stationary noises), that would closely match costly and time-consuming subjective user tests. the article describes results of a comparison between subjective scores obtained using mushra methodology vs. automated evaluation with objective metrics i.e. polqa, 3quest, stoi & estoi, on a set of recordings processed by 2 different denoising algorithms for close & far speaker distance. correlation coefficient is calculated between subjective scores and examined metrics. the results are based on recordings simulated using an in-house simulation toolchain, based on impulse responses from actual laptop device used in low reverb quiet room.},}
TY - paper
TI - Overview of speech quality metrics in terms of automated evaluation of signal denoising in a presence of non-stationary noise
SP -
EP -
AU - Duzinkiewicz, Karol
AU - Koszewski, Damian
AU - Pietrusinska, Kamila
AU - Trella, Pawel
PY - 2020
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - October 2020
TY - paper
TI - Overview of speech quality metrics in terms of automated evaluation of signal denoising in a presence of non-stationary noise
SP -
EP -
AU - Duzinkiewicz, Karol
AU - Koszewski, Damian
AU - Pietrusinska, Kamila
AU - Trella, Pawel
PY - 2020
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - October 2020
AB - Recent developments in neural network-based speech enhancement ask for a robust subjective metric that can be used for comparing performance of a different noise suppression algorithms (for non-stationary noises), that would closely match costly and time-consuming subjective user tests. The article describes results of a comparison between subjective scores obtained using MUSHRA methodology vs. automated evaluation with objective metrics i.e. POLQA, 3QUEST, STOI & ESTOI, on a set of recordings processed by 2 different denoising algorithms for close & far speaker distance. Correlation coefficient is calculated between subjective scores and examined metrics. The results are based on recordings simulated using an in-house simulation toolchain, based on impulse responses from actual laptop device used in low reverb quiet room.
Recent developments in neural network-based speech enhancement ask for a robust subjective metric that can be used for comparing performance of a different noise suppression algorithms (for non-stationary noises), that would closely match costly and time-consuming subjective user tests. The article describes results of a comparison between subjective scores obtained using MUSHRA methodology vs. automated evaluation with objective metrics i.e. POLQA, 3QUEST, STOI & ESTOI, on a set of recordings processed by 2 different denoising algorithms for close & far speaker distance. Correlation coefficient is calculated between subjective scores and examined metrics. The results are based on recordings simulated using an in-house simulation toolchain, based on impulse responses from actual laptop device used in low reverb quiet room.
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