Using topic-noise models to generate domain-specific topics across data sources.

Domain-specific document collections, such as data sets about the COVID-19 pandemic, politics, and sports, have become more common as platforms grow and develop better ways to connect people whose interests align. These data sets come from many different sources, ranging from traditional sources like open-ended surveys and newspaper articles to one of the dozens of online social media platforms. Most topic models are equipped to generate topics from one or more of these data sources, but models rarely work well across all types of documents. The main problem that many models face is the varying noise levels inherent in different types of documents. We propose topic-noise models, a new type of topic model that jointly models topic and noise distributions to produce a more accurate, flexible representation of documents regardless of their origin and varying qualities. Our topic-noise model, Topic Noise Discriminator (TND) approximates topic and noise distributions side-by-side with the help of word embedding spaces. While topic-noise models are important for the types of short, noisy documents that often originate on social media platforms, TND can also be used with more traditional data sources like newspapers. TND itself generates a noise distribution that when ensembled with other generative topic models can produce more coherent and diverse topic sets. We show the effectiveness of this approach using Latent Dirichlet Allocation (LDA), and demonstrate the ability of TND to improve the quality of LDA topics in noisy document collections. Finally, researchers are beginning to generate topics using multiple sources and finding that they need a way to identify a core set based on text from different sources. We propose using cross-source topic blending (CSTB), an approach that maps topics sets to an s-partite graph and identifies core topics that blend topics from across s sources by identifying subgraphs with certain linkage properties. We demonstrate the effectiveness of topic-noise models and CSTB empirically on large real-world data sets from multiple domains and data sources.

Effects of the COVID-19 Pandemic on the noise exposure of population around Split Airport

Airports negatively affect the environment by generating annoying aircraft noise. In practice, negative noise effect due to constant increase of air traffic is being compensated for through improved approach and departure procedures as well as new methods of noise reduction, mainly at the noise source itself. The emergence of COVID-19 pandemic significantly reduced air traffic throughout the world, which had a positive effect on the noise exposure of the people living around airports. This research is analyzing the effect of the COVID-19 on the noise exposure of the population living around Split Airport. The analysis was conducted based on the comparison of the noise maps for the peak days of 2019 and 2020, created in the Integrated Noise Model. The estimated number of people highly annoyed during the day for the peak day of 2020 was reduced by 14.7% compared to 2019, while the estimated number of people highly sleep-disturbed decreased by 62.3%. © 2022 The Authors. Published by ELSEVIER B.V.

The Sound of a Circular City: Towards a Circularity-Driven Quietness.

The circular economy paradigm can be beneficial for urban sustainability by eliminating waste and pollution, by circulating products and materials and by regenerating nature. Furthermore, under an urban circular development scheme, environmental noise can be designed out. The current noise control policies and actions, undertaken at a source-medium-receiver level, present a linearity with minimum sustainability co-benefits. A circular approach in noise control strategies and in soundscape design could offer numerous ecologically related co-benefits. The global literature documenting the advantages of the implementation of circular economy in cities has highlighted noise mitigation as a given benefit. Research involving circular economy actions such as urban green infrastructure, green walls, sustainable mobility systems and electro-mobility has acknowledged reduced noise levels as a major circularity outcome. In this research paper, we highlight the necessity of a circularity and bioeconomy approach in noise control. To this end, a preliminary experimental noise modeling study was conducted to showcase the acoustic benefits of green walls and electric vehicles in a medium-sized urban area of a Mediterranean island. The results indicate a noise level reduction at 4 dB(A) when simulating the introduction of urban circular development actions.

Remote work: Aircraft noise implications, prediction, and management in the built environment.

The COVID-19 pandemic has greatly changed workplace management. Most workplaces have adopted the work-from-home policy to minimize the risk of community spread. Consequently, housing estates remain largely occupied during office hours. Since some housing estates are situated in the vicinity of an airport, noise pollution resulted from the takeoff and landing of aircraft is now more noticed by residents, causing annoyance. This problem would be most acute for those located directly under the flight path. Before the pandemic, such aircraft operations had lower effect on the residents because most of them were not at home but at workplaces. Evidently, it is timely that more emphasis should now be placed during urban planning to predict and minimize aircraft noise in the built environment. This article first defines the aircraft noise metrics commonly used to assess environmental impact. Preceded by an overview of how aircraft noise affects the built environment, this article reviews how various aircraft noise prediction models have been used in urban planning. Lastly, this article reviews how aircraft noise can be managed for better acoustic comfort of the residents. Anticipating the adoption of hybrid work arrangement moving forward, this article aims to provide urban planning professionals with an avenue to understand how aircraft noise can negatively affect the built environment, which, in turn, justify why prediction and management of aircraft noise should be emphasized from the outset of urban planning.

Urbanization effect on Hyderabad seismic station

Seismographs record earthquakes and also record various types of noise, including anthropogenic noise. In the present study, we analyse the influence of the lockdown due to COVID-19 on the ground motion at CSIR-NGRI HYB Seismological Observatory, Hyderabad. We analyse the noise recorded a week before and after the implementation of lockdown by estimating the probability density function of seismic power spectral density and by constructing the daily spectrograms. We find that at low frequency (<1 Hz), where the noise is typically dominated by naturally occurring microseismic noise, a reduction of ~2 dB for secondary microseisms (7–3 s) and at higher frequency (1–10 Hz) a reduction of ~6 dB was observed during the lockdown period. The reduction in higher frequencies corresponding to anthropogenic noise sources led to improving the SNR (signal-to-noise ratio) by a factor of 2 which is the frequency bandwidth of the microearthquakes leading to the identification of microearthquakes with Ml around 3 from epicentral distances of 180 km.