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. 2022 Feb;151(2):911.
doi: 10.1121/10.0009324.

Multidimensional analyses of the noise impacts of COVID-19 lockdown

Pierre Aumond  1 , Arnaud Can  1 , Mathieu Lagrange  2 , Felix Gontier  2 , Catherine Lavandier  3
Affiliations

Multidimensional analyses of the noise impacts of COVID-19 lockdown

Pierre Aumond et al. J Acoust Soc Am. 2022 Feb.

Abstract

As part of the Agence Nationale de Recherche Caractérisation des ENvironnements SonorEs urbains (Characterization of urban sound environments) project, a questionnaire was sent in January 2019 to households in a 1 km2 study area in the city of Lorient, France, to which about 318 responded. The main objective of this questionnaire was to collect information about the inhabitants' perception of the sound environments in their neighborhoods, streets, and dwellings. In the same study area, starting mid-2019, about 70 sensors were continuously positioned, and 15 of them were selected for testing sound source recognition models. The French lockdown due to the COVID-19 crisis occurred during the project, and the opportunity was taken to send a second questionnaire during April 2020. About 31 of the first 318 first survey respondents answered this second questionnaire. This unique longitudinal dataset, both physical and perceptual, allows the undertaking of an analysis from different perspectives of such a period. The analysis reveals the importance of integrating source recognition tools, soundscape observation protocol, in addition to physical level analysis, to accurately describe the changes in the sound environment.

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Figures

FIG. 1.
FIG. 1.
(Color online) The CENSE study area. The labels correspond to the selected sensors (n = 15). The labels circled in red correspond to the stations that we focus on more during the analysis. The heat map displays the density of the responses to the questionnaire in the first call (n = 318).
FIG. 2.
FIG. 2.
(Color online) An example of the predicted source activity for a simulated sound scene. From the mixed scene (top), the model infers the presence of seven sound sources along the time (shaded areas). The separated waveform contributions for each source are shown for illustrative purposes and hidden to the predictor.
FIG. 3.
FIG. 3.
The empirical variogram of the Sil parameter along the Euclidian distance. The fitted parameters are nugget (1.0), range (50 m), and sill (1.5).
FIG. 4.
FIG. 4.
(Color online) The median and interquartile range of LAeq,1s (10 min, every hour) for the seven days of the week and two focus stations, respectively, located (a) in a quiet residential area and (b) on a boulevard for each period of measurement M1 and M2 are shown.
FIG. 5.
FIG. 5.
(Color online) For the 15 stations, (a) the pre-lockdown LAeq,M1 and (b) its difference with the period during the lockdown LAeq,M1 – LAeq,M2 are depicted.
FIG. 6.
FIG. 6.
(Color online) The average hourly median of the presence time of the six estimated parameters for the two focus stations over the week period (left column, boulevard; right column quiet residential area) and the presence time of the six estimated parameters are shown.
FIG. 7.
FIG. 7.
(Color online) The relative change between the average estimated time of presence of sound sources between the periods M1 and M2 (M2 - M1)/M1 is depicted.
See this image and copyright information in PMC

References

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