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A method for evaluating different audio systems with respect to their spatial reproduction accuracy is described based on binaural auditory feature classification. The classifier is trained to act as expert listener judging system spatial quality and achieves high accuracy for a reference ideal system under anechoic conditions. The trained classifier is then employed to assess different suboptimal reproduction setups and listening conditions. The spatial accuracy was assessed with respect to this reference with respect to the panned image, image localization accuracy, and the sweet spot area spread. For 2 channel stereo reproduction, the study used loudspeakers of different directivity under anechoic or varying reverberant room conditions. The work also assesses the relative effects of upmixing stereo for 5 channel reproduction.
Author (s): Kamaris, Gavriil; Karlos, Stamatis; Terpinas, Stergios; Koutsaidis, Dimitris; Mourjopoulos, John
Affiliation:
University of Patras, Patras, Greece
(See document for exact affiliation information.)
AES Convention: 142
Paper Number:9761
Publication Date:
2017-05-06
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Session subject:
Audio Analysis and Synthesis
Permalink: https://aes2.org/publications/elibrary-page/?id=18637
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Kamaris, Gavriil; Karlos, Stamatis; Terpinas, Stergios; Koutsaidis, Dimitris; Mourjopoulos, John; 2017; Audio System Spatial Image Evaluation via Binaural Feature Classification [PDF]; University of Patras, Patras, Greece; Paper 9761; Available from: https://aes2.org/publications/elibrary-page/?id=18637
Kamaris, Gavriil; Karlos, Stamatis; Terpinas, Stergios; Koutsaidis, Dimitris; Mourjopoulos, John; Audio System Spatial Image Evaluation via Binaural Feature Classification [PDF]; University of Patras, Patras, Greece; Paper 9761; 2017 Available: https://aes2.org/publications/elibrary-page/?id=18637
@article{kamaris2017audio,
author={kamaris gavriil and karlos stamatis and terpinas stergios and koutsaidis dimitris and mourjopoulos john},
journal={journal of the audio engineering society},
title={audio system spatial image evaluation via binaural feature classification},
year={2017},
number={9761},
month={may},}