Analysing Interlinked Frequency Dynamics of the Urban Acoustic Environment

Timo Haselhoff (), Tobias Braun, Jonas Hornberg, Bryce T. Lawrence, Salman Ahmed, Dietwald Gruehn and Susanne Moebus
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Timo Haselhoff: Institute for Urban Public Health (InUPH), University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany
Tobias Braun: Complexity Science, Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany
Jonas Hornberg: Institute for Urban Public Health (InUPH), University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany
Bryce T. Lawrence: Department of Spatial Planning, TU Dortmund University, 44227 Dortmund, Germany
Salman Ahmed: Institute for Urban Public Health (InUPH), University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany
Dietwald Gruehn: Department of Spatial Planning, TU Dortmund University, 44227 Dortmund, Germany
Susanne Moebus: Institute for Urban Public Health (InUPH), University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany

IJERPH, 2022, vol. 19, issue 22, 1-16

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.

Keywords: urban soundscape; acoustic environment; frequency correlation matrices; time–frequency domain; urban acoustics (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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