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Journal Article

Analysing Interlinked Frequency Dynamics of the Urban Acoustic Environment

Authors

Haselhoff,  Timo
External Organizations;

/persons/resource/tobraun

Braun,  Tobias
Potsdam Institute for Climate Impact Research;

Hornberg,  Jonas
External Organizations;

Lawrence,  Bryce T.
External Organizations;

Ahmed,  Salman
External Organizations;

Gruehn,  Dietwald
External Organizations;

Moebus,  Susanne
External Organizations;

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Fulltext (public)

27574oa.pdf
(Publisher version), 4MB

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Citation

Haselhoff, T., Braun, T., Hornberg, J., Lawrence, B. T., Ahmed, S., Gruehn, D., Moebus, S. (2022): Analysing Interlinked Frequency Dynamics of the Urban Acoustic Environment. - International Journal of Environmental Research and Public Health, 19, 22, 15014.
https://doi.org/10.3390/ijerph192215014


Cite as: https://publications.pik-potsdam.de/pubman/item/item_27574
Abstract
As sustainable metropolitan regions require more densely built-up areas, a comprehensive understanding of the urban acoustic environment (AE) is needed. However, comprehensive datasets of the urban AE and well-established research methods for the AE are scarce. Datasets of audio recordings tend to be large and require a lot of storage space as well as computationally expensive analyses. Thus, knowledge about the long-term urban AE is limited. In recent years, however, these limitations have been steadily overcome, allowing a more comprehensive analysis of the urban AE. In this respect, the objective of this work is to contribute to a better understanding of the time–frequency domain of the urban AE, analysing automatic audio recordings from nine urban settings over ten months. We compute median power spectra as well as normalised spectrograms for all settings. Additionally, we demonstrate the use of frequency correlation matrices (FCMs) as a novel approach to access large audio datasets. Our results show site-dependent patterns in frequency dynamics. Normalised spectrograms reveal that frequency bins with low power hold relevant information and that the AE changes considerably over a year. We demonstrate that this information can be captured by using FCMs, which also unravel communities of interlinked frequency dynamics for all settings.