Towards an interdisciplinary formalization of soundscapes.

Soundscapes have been studied by researchers from various disciplines, each with different perspectives, approaches, and terminologies. Consequently, the research field determines the actual concept of a specific soundscape with the associated components and also affects the definition itself. This complicates interdisciplinary communication and comparison of results, especially when research areas are involved which are not directly focused on soundscapes. For this reason, we present a formalization that aims to be independent of the concepts from the various disciplines, with the goal of being able to capture the heterogeneous data structure in one layered model. Our model consists of time-dependent sound sources and geodata that influence the acoustic composition of a soundscape represented by our sensor function. Using a case study, we present the application of our formalization by classifying land use types. For this we analyze soundscapes in the form of recordings from different devices at 23 different locations using three-dimensional convolutional neural networks and frequency correlation matrices. In our results, we present that soundscapes can be grouped into classes, but the given land use categories do not have to correspond to them.

The Role of Traffic Volume on Sound Pressure Level Reduction before and during COVID-19 Lockdown Measures-A Case Study in Bochum, Germany.

During the SARS-CoV-2 pandemic, sound pressure levels (SPL) decreased because of lockdown measures all over the world. This study aims to describe SPL changes over varying lockdown measure timeframes and estimate the role of traffic on SPL variations. To account for different COVID-19 lockdown measures, the timeframe during the pandemic was segmented into four phases. To analyze the association between a-weighted decibels (dB(A)) and lockdown phases relative to the pre-lockdown timeframe, we calculated a linear mixed model, using 36,710 h of recording time. Regression coefficients depicting SPL changes were compared, while the model was subsequently adjusted for wind speed, rainfall, and traffic volume. The relative adjusted reduction of during pandemic phases to pre-pandemic levels ranged from -0.99 dB(A) (CI: -1.45; -0.53) to -0.25 dB(A) (CI: -0.96; 0.46). After controlling for traffic volume, we observed little to no reduction (-0.16 dB(A) (CI: -0.77; 0.45)) and even an increase of 0.75 dB(A) (CI: 0.18; 1.31) during the different lockdown phases. These results showcase the major role of traffic regarding the observed reduction. The findings can be useful in assessing measures to decrease noise pollution for necessary future population-based prevention.

Analysing Interlinked Frequency Dynamics of the Urban Acoustic Environment.

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.

Impact of the COVID-19 Lockdown Measures on Noise Levels in Urban Areas-A Pre/during Comparison of Long-Term Sound Pressure Measurements in the Ruhr Area, Germany.

BACKGROUND: A major source of noise pollution is traffic. In Germany, the SARS-CoV-2 lockdown caused a substantial decrease in mobility, possibly affecting noise levels. The aim is to analyze the effects of the lockdown measures on noise levels in the densely populated Ruhr Area. We focus on the analysis of noise levels before and during lockdown considering different land use types, weekdays, and time of day. METHODS: We used data from 22 automatic sound devices of the SALVE (Acoustic Quality and Health in Urban Environments) project, running since 2019 in Bochum, Germany. We performed a pre/during lockdown comparison of A-weighted equivalent continuous sound pressure levels. The study period includes five weeks before and five weeks during the SARS-CoV-2 induced administrative lockdown measures starting on 16 March 2020. We stratified our data by land use category (LUC), days of the week, and daytime. RESULTS: We observed highest noise levels pre-lockdown in the 'main street' and 'commercial areas' (68.4 ± 6.7 dB resp. 61.0 ± 8.0 dB), while in 'urban forests' they were lowest (50.9 ± 6.6 dB). A distinct mean overall noise reduction of 5.1 dB took place, with noise reductions occurring in each LUC. However, the magnitude of noise levels differed considerably between the categories. Weakest noise reductions were found in the 'main street' (3.9 dB), and strongest in the 'urban forest', 'green space', and 'residential area' (5.9 dB each). CONCLUSIONS: Our results are in line with studies from European cities. Strikingly, all studies report noise reductions of about 5 dB. Aiming at a transformation to a health-promoting urban mobility can be a promising approach to mitigating health risks of noise in cities. Overall, the experiences currently generated by the pandemic offer data for best practices and policies for the development of healthy urban transportation-the effects of a lower traffic and more tranquil world were experienced firsthand by people during this time.