Investigating urban soundscapes of the COVID-19 lockdown: A predictive soundscape modeling approach

Abstract

The unprecedented lockdowns resulting from COVID-19 in spring 2020 triggered changes in human activities in public spaces. A predictive modeling approach was developed to characterize the changes in the perception of the sound environment when people could not be surveyed. Building on a database of soundscape questionnaires ( N = 1,136) and binaural recordings ( N = 687) collected in 13 locations across London and Venice during 2019, new recordings ( N = 571) were made in the same locations during the 2020 lockdowns. Using these 30-s-long recordings, linear multilevel models were developed to predict the soundscape pleasantness ( ) and eventfulness ( ) during the lockdown and compare the changes for each location. The performance was above average for comparable models. An online listening study also investigated the change in the sound sources within the spaces. Results indicate (1) human sounds were less dominant and natural sounds more dominant across all locations; (2) contextual information is important for predicting pleasantness but not for eventfulness; (3) perception shifted toward less eventful soundscapes and to more pleasant soundscapes for previously traffic-dominated locations but not for human- and natural-dominated locations. This study demonstrates the usefulness of predictive modeling and the importance of considering contextual information when discussing the impact of sound level reductions on the soundscape.

Effects of Soundscape Complexity on Urban Noise Annoyance Ratings: A Large-Scale Online Listening Experiment

“…While participants were completing this survey, a binaural recording of approximately 30 s was made to capture the exact sound environment to which the participant was exposed. In total, 13 public spaces were assessed (including surveys) throughout 2019 and additional recordings were collected in the same locations during the 2020 COVID-19 lockdowns (not including the participant surveys) [ 34 ]. In total, this dataset includes 2519 recordings with a duration between 8 and 122 s. For the purposes of this study, only the calibrated binaural recordings are used from the ISD, and not any additional information about the in situ participants, locations, or soundscape assessments.…”

Singapore Soundscape Site Selection Survey (S5): Identification of Characteristic Soundscapes of Singapore via Weighted k-Means Clustering

“…Since the study was conducted amidst measures taken to minimize the spread of COVID-19, we additionally instructed participants to identify locations based on memory and experience prior to the implementation of these measures. Since COVID-19 mitigation measures have altered the perception of in-situ urban soundscapes [27], the instruction to identify locations prior to the implementation of these measures would make the locations applicable even after they have been lifted.…”

How the COVID-19 Pandemic Muted and Remixed the World’s Acoustics for a While

“…A rather different approach was presented in Mitchell et al [70]. This study is based on the modeling of pleasantness and eventfulness metrics defined in the ISO 12913 standard for the evaluation of a soundscape [71,72].…”