Issue |
Acta Acust.
Volume 6, 2022
|
|
---|---|---|
Article Number | 46 | |
Number of page(s) | 10 | |
Section | Environmental Noise | |
DOI | https://doi.org/10.1051/aacus/2022042 | |
Published online | 07 October 2022 |
Scientific Article
On the identification and assessment of underlying acoustic dimensions of soundscapes
Institute of Communications Technology, Leibniz University Hannover, Appelstr. 9A, 30167 Hannover, Germany
* Corresponding author: jakob.bergner@ikt.uni-hannover.de
Received:
6
July
2022
Accepted:
14
September
2022
The concept of soundscapes according to ISO 12913-1/-2/-3 proposes a descriptive framework based on a triangulation between the entities acoustic environment, person and context. While research on the person-related dimensions is well established, there is not yet complete agreement on the relevant indicators and dimensions for the pure description of acoustic environments. Therefore, this work attempts to identify acoustic dimensions that actually vary between different acoustic environments and thus can be used to characterize them. To this end, an exploratory, data-based approach was taken. A database of Ambisonics soundscape recordings (approx. 12.5 h) was first analyzed using a variety of signal-based acoustic indicators (Ni = 326) within the categories loudness, quality, spaciousness and time. Multivariate statistical methods were then applied to identify compound and interpretable acoustic dimensions. The interpretation of the results reveals 8 independent dimensions “Loudness”, “Directivity”, “Timbre”, “High-Frequency Timbre”, “Dynamic Range”, “High-Frequency Amplitude Modulation”, “Loudness Progression” and “Mid-High-Frequency Amplitude Modulation” to be statistically relevant. These derived latent acoustic dimensions explain 48.76% of the observed total variance and form a physical basis for the description of acoustic environments. Although all baseline indicators were selected for perceptual reasons, validation must be done through appropriate listening tests in future.
Key words: Soundscape / Underlying acoustic dimensions / Statistical signal processing / Multivariate statistics
© The Author(s), published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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