F. Rumsey, "Quality, Emotion, and Machines," J. Audio Eng. Soc., vol. 69, no. 11, pp. 890-894, (2021 November.). doi:
F. Rumsey, "Quality, Emotion, and Machines," J. Audio Eng. Soc., vol. 69 Issue 11 pp. 890-894, (2021 November.). doi:
Abstract: As research into the features of audio quality continues, the emphasis is increasingly on understanding the relationship with human emotions and how machines can be taught to do human-like analysis or synthesis. Separating the effects of audio content from those of its quality is a persistent challenge in this type of work.
@article{rumsey2021quality,,
author={rumsey, francis},
journal={journal of the audio engineering society},
title={quality, emotion, and machines},
year={2021},
volume={69},
number={11},
pages={890-894},
doi={},
month={november},}
@article{rumsey2021quality,,
author={rumsey, francis},
journal={journal of the audio engineering society},
title={quality, emotion, and machines},
year={2021},
volume={69},
number={11},
pages={890-894},
doi={},
month={november},
abstract={as research into the features of audio quality continues, the emphasis is increasingly on understanding the relationship with human emotions and how machines can be taught to do human-like analysis or synthesis. separating the effects of audio content from those of its quality is a persistent challenge in this type of work.},}
TY - feature
TI - Quality, Emotion, and Machines
SP - 890
EP - 894
AU - Rumsey, Francis
PY - 2021
JO - Journal of the Audio Engineering Society
IS - 11
VO - 69
VL - 69
Y1 - November 2021
TY - feature
TI - Quality, Emotion, and Machines
SP - 890
EP - 894
AU - Rumsey, Francis
PY - 2021
JO - Journal of the Audio Engineering Society
IS - 11
VO - 69
VL - 69
Y1 - November 2021
AB - As research into the features of audio quality continues, the emphasis is increasingly on understanding the relationship with human emotions and how machines can be taught to do human-like analysis or synthesis. Separating the effects of audio content from those of its quality is a persistent challenge in this type of work.
As research into the features of audio quality continues, the emphasis is increasingly on understanding the relationship with human emotions and how machines can be taught to do human-like analysis or synthesis. Separating the effects of audio content from those of its quality is a persistent challenge in this type of work.
