"Crisis" or "opportunity"? COVID-19 pandemic's impact on environmentally sound invention efficiency in China.

Introduction: The environmentally sound invention (ESI) is a "bridge" between environmental sound technologies (ESTs) and green productions. This study investigates the COVID-19 pandemic's impact on ESI efficiency using a multi-methods model in three stages. Methods: The ESI efficiency is measured using the Slack-Based Measure (SBM) method in the first stage. By excluding the environmental effect of the pandemic on each province using the stochastic frontier analysis (SFA) model's results in the second stage, this study compares the ESI efficiency change with or without the influence of the pandemic in the third stage. Results: The results show that the pandemic can be a "crisis" in the short term, but an "opportunity" in the long term. First, the SBM efficiency results in the first stage show a decrease in the number of the average efficient provinces in which the pandemic is more severe during 2020-2021. Second, results of the spatial Tobit and SFA models provide evidence that the COVID-19 pandemic negatively impacts the ESI efficiency during 2020, this impact is decreasing in 2021, and this impact has a spatial diffusion effect. Discussion: Based on these results, this study discussed the theoretical and political implications. This paper enriches the knowledge of ESTs research and development by proposing a three-stage approach with multi-methods to investigate the influence of the pandemic's impact on ESI efficiency.

The sonic strategies and technologies of listening alone together in The World According to Sound’s Outside In: A Communal Listening Series

This article examines three dimensions of Chris Hoff and Sam Harnett’s practices for producing, sharing and listening to audio in collective and social ways for The World According to Sound’s Outside In: the sonic strategies and soundscape design used to engage communal and collective listening, how Outside In adapts and transforms traditional paradigms using the broadcast medium of the podcast to aesthetically engage with liveness and the corporeality of sound, and how the COVID-19 pandemic afforded space for ‘unpopular’ soundwork based on everyday aural architectures (e.g., field recordings, ambient music, experimental music based on everyday sounds, soundscape collages) that are popular, as in, of the community. Using varied examples drawn from The World According to Sound’s soundwork, I illustrate a particular set of sonic strategies to imagine sonic space, listen relationally to sound events, and enact a sociality of collective listening.

Children's Activity Classification for Domestic Risk Scenarios Using Environmental Sound and a Bayesian Network.

Children's healthcare is a relevant issue, especially the prevention of domestic accidents, since it has even been defined as a global health problem. Children's activity classification generally uses sensors embedded in children's clothing, which can lead to erroneous measurements for possible damage or mishandling. Having a non-invasive data source for a children's activity classification model provides reliability to the monitoring system where it is applied. This work proposes the use of environmental sound as a data source for the generation of children's activity classification models, implementing feature selection methods and classification techniques based on Bayesian networks, focused on the recognition of potentially triggering activities of domestic accidents, applicable in child monitoring systems. Two feature selection techniques were used: the Akaike criterion and genetic algorithms. Likewise, models were generated using three classifiers: naive Bayes, semi-naive Bayes and tree-augmented naive Bayes. The generated models, combining the methods of feature selection and the classifiers used, present accuracy of greater than 97% for most of them, with which we can conclude the efficiency of the proposal of the present work in the recognition of potentially detonating activities of domestic accidents.

Descriptive characterization of sound levels in an environmental justice city before and during a global pandemic.

Many environmental justice communities face elevated exposures to multiple stressors, given biases in urban and environmental policy and planning. This paper aims to evaluate sound level exposure in a densely populated environmental justice city in close proximity to major roadways, a nearby airport and high levels of industrial activity. In this study we collected various sound level metrics to evaluate the loudness and frequency composition of the acoustical environment in Chelsea, Massachusetts, USA. A total of 29 week-long sites were collected from October 2019 to June 2020, a time period that also included the influence of the COVID-19 pandemic, which drastically altered activity patterns and corresponding sound level exposures. We found that Chelsea is exposed to high levels of sound, both day and night (65 dB (A), and 80 dB and 90 dB for low frequency, and infrasound sound levels). A spectral analysis shows that 63 Hz was the dominant frequency. Distance to major roads and flight activity (both arrivals and departures) were most strongly correlated with all metrics, most notably with metrics describing contributing from lower frequencies. Overall, we found similar patterns during the COVID-19 pandemic but at levels up to 10 dB lower. Our results demonstrate the importance of noise exposure assessments in environmental justice communities and the importance of using additional metrics to describe communities inundated with significant air, road, and industrial sound levels. It also provides a snapshot of how much quieter communities can be with careful and intentional urban and environmental policy and planning.