Hou, Y;
Mitchell, A;
Ren, Q;
Aletta, F;
Kang, J;
Botteldooren, D;
(2023)
EXPLORING ANNOYANCE IN A SOUNDSCAPE CONTEXT BY JOINT PREDICTION OF SOUND SOURCE AND ANNOYANCE.
In:
Proceedings of the 10th Convention of the European Acoustics Association Forum Acusticum 2023.
(pp. pp. 921-924).
European Acoustics Association: Politecnico di Torino Torino, Italy.
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Abstract
Soundscape, the sonic environment as perceived and understood by people, is a conglomerate of different sounds. It has been established that its appraisal by instantaneous annoyance is not solely determined by its calculated loudness, but also by recognised sounds. Hence, most previous research on annoyance has focused on single-source environments. Audio analytics aims at detecting and classifying sound sources, but does not explore human perception of these. This paper proposes a dual-input model to simultaneously perform sound source classification (SSC) and human annoyance rating prediction (ARP). The model takes mel features and root-mean-square value (rms) features as input, and uses convolutional blocks to extract high-level acoustic features. These are used to predict sound source classes and to estimate the human annoyance rating for the whole fragment. Experiments on the DeLTA dataset show that: 1) models using mel features and rms features outperform models using only one of them; 2) The proposed model achieves a SSC accuracy of 90.06%, and an ARP (scale 1 to 10) root mean square error of 1.05.
Type: | Proceedings paper |
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Title: | EXPLORING ANNOYANCE IN A SOUNDSCAPE CONTEXT BY JOINT PREDICTION OF SOUND SOURCE AND ANNOYANCE |
ISBN-13: | 9788888942674 |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.61782/fa.2023.0713 |
Publisher version: | https://www.doi.org/10.61782/fa.2023.0713 |
Language: | English |
Additional information: | This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources |
URI: | https://discovery.ucl.ac.uk/id/eprint/10192342 |
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