Subjective Rating and PESQ Prediction of Listener Echo and Duplex Impairments
×
Cite This
Citation & Abstract
H. Müsch, "Subjective Rating and PESQ Prediction of Listener Echo and Duplex Impairments," J. Audio Eng. Soc., vol. 67, no. 3, pp. 124-134, (2019 March.). doi: https://doi.org/10.17743/jaes.2018.0076
H. Müsch, "Subjective Rating and PESQ Prediction of Listener Echo and Duplex Impairments," J. Audio Eng. Soc., vol. 67 Issue 3 pp. 124-134, (2019 March.). doi: https://doi.org/10.17743/jaes.2018.0076
Abstract: This study shows that PESQ can be used as a tool to evaluate degradations from listener echo and duplex impairments caused by echo-mitigation algorithms such as echo cancellation or echo suppression. Both the PESQ-based metric and standards 3GPP TS 26.132 and P.502 share the approach of testing with real speech and comparing an impaired signal to an unimpaired reference. However, unlike 3GPP/P.502, PESQ provides tools for accurate time alignment of the signals that function even with temporally varying delay (jitter) and thus allow measurement in IP-based networks. Moreover, the PESQ metric follows the common practice of calculating PESQ values for any test condition with several speech samples, which stabilizes the quality estimate. In contrast 3GPP prescribes the use of a single test signal, which causes potentially misleading sampling error. Finally, the well-developed perceptual model underlying PESQ generates a perceptually relevant one-dimensional result. This is suitable for benchmark or regression testing. In contrast 3GPP and P.502 use only rudimentary perceptual models or no models at all and generate multidimensional results that are unwieldy when used for performance comparison or tracking
@article{müsch2019subjective,
author={müsch, hannes},
journal={journal of the audio engineering society},
title={subjective rating and pesq prediction of listener echo and duplex impairments},
year={2019},
volume={67},
number={3},
pages={124-134},
doi={https://doi.org/10.17743/jaes.2018.0076},
month={march},}
@article{müsch2019subjective,
author={müsch, hannes},
journal={journal of the audio engineering society},
title={subjective rating and pesq prediction of listener echo and duplex impairments},
year={2019},
volume={67},
number={3},
pages={124-134},
doi={https://doi.org/10.17743/jaes.2018.0076},
month={march},
abstract={this study shows that pesq can be used as a tool to evaluate degradations from listener echo and duplex impairments caused by echo-mitigation algorithms such as echo cancellation or echo suppression. both the pesq-based metric and standards 3gpp ts 26.132 and p.502 share the approach of testing with real speech and comparing an impaired signal to an unimpaired reference. however, unlike 3gpp/p.502, pesq provides tools for accurate time alignment of the signals that function even with temporally varying delay (jitter) and thus allow measurement in ip-based networks. moreover, the pesq metric follows the common practice of calculating pesq values for any test condition with several speech samples, which stabilizes the quality estimate. in contrast 3gpp prescribes the use of a single test signal, which causes potentially misleading sampling error. finally, the well-developed perceptual model underlying pesq generates a perceptually relevant one-dimensional result. this is suitable for benchmark or regression testing. in contrast 3gpp and p.502 use only rudimentary perceptual models or no models at all and generate multidimensional results that are unwieldy when used for performance comparison or tracking},}
TY - report
TI - Subjective Rating and PESQ Prediction of Listener Echo and Duplex Impairments
SP - 124
EP - 134
AU - Müsch, Hannes
PY - 2019
JO - Journal of the Audio Engineering Society
IS - 3
VO - 67
VL - 67
Y1 - March 2019
TY - report
TI - Subjective Rating and PESQ Prediction of Listener Echo and Duplex Impairments
SP - 124
EP - 134
AU - Müsch, Hannes
PY - 2019
JO - Journal of the Audio Engineering Society
IS - 3
VO - 67
VL - 67
Y1 - March 2019
AB - This study shows that PESQ can be used as a tool to evaluate degradations from listener echo and duplex impairments caused by echo-mitigation algorithms such as echo cancellation or echo suppression. Both the PESQ-based metric and standards 3GPP TS 26.132 and P.502 share the approach of testing with real speech and comparing an impaired signal to an unimpaired reference. However, unlike 3GPP/P.502, PESQ provides tools for accurate time alignment of the signals that function even with temporally varying delay (jitter) and thus allow measurement in IP-based networks. Moreover, the PESQ metric follows the common practice of calculating PESQ values for any test condition with several speech samples, which stabilizes the quality estimate. In contrast 3GPP prescribes the use of a single test signal, which causes potentially misleading sampling error. Finally, the well-developed perceptual model underlying PESQ generates a perceptually relevant one-dimensional result. This is suitable for benchmark or regression testing. In contrast 3GPP and P.502 use only rudimentary perceptual models or no models at all and generate multidimensional results that are unwieldy when used for performance comparison or tracking
This study shows that PESQ can be used as a tool to evaluate degradations from listener echo and duplex impairments caused by echo-mitigation algorithms such as echo cancellation or echo suppression. Both the PESQ-based metric and standards 3GPP TS 26.132 and P.502 share the approach of testing with real speech and comparing an impaired signal to an unimpaired reference. However, unlike 3GPP/P.502, PESQ provides tools for accurate time alignment of the signals that function even with temporally varying delay (jitter) and thus allow measurement in IP-based networks. Moreover, the PESQ metric follows the common practice of calculating PESQ values for any test condition with several speech samples, which stabilizes the quality estimate. In contrast 3GPP prescribes the use of a single test signal, which causes potentially misleading sampling error. Finally, the well-developed perceptual model underlying PESQ generates a perceptually relevant one-dimensional result. This is suitable for benchmark or regression testing. In contrast 3GPP and P.502 use only rudimentary perceptual models or no models at all and generate multidimensional results that are unwieldy when used for performance comparison or tracking
