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A specific smartphone application was developed to collect perceptive and acoustic data in Paris. About 3400 questionnaires were analyzed, regarding the global sound environment characterization, the perceived loudness of some emergent sources and the presence time ratio of sources that do not emerge from the background. Sound pressure level was recorded each second from the mobile phone's microphone during a 10-min period. The aim of this study is to propose indicators of urban sound quality based on linear regressions with perceptive variables. A cross validation of the quality models extracted from Paris data was carried out by conducting the same survey in Milan. The proposed sound quality general model is correlated with the real perceived sound quality (72%). Another model without visual amenity and familiarity is 58% correlated with perceived sound quality. In order to improve the sound quality indicator, a site classification was performed by Kohonen's Artificial Neural Network algorithm, and seven specific class models were developed. These specific models attribute more importance on source events and are slightly closer to the individual data than the global model. In general, the Parisian models underestimate the sound quality of Milan environments assessed by Italian people.

Mapping the pleasantness of an urban environment is an alternative approach, closer to the city dweller's perception, than standardized sound levels cartography. This study reports on modeling pleasantness in urban context using perceptual assessments and sound measurements for specific locations during an urban walk. These assessments have been collected from four groups of approximately ten participants on 19 different assessment locations, along a 2,1 km-long path traveled in both directions. Simultaneously, ⅓ octave band sound levels and audio were recorded. Perceptual and physical models of pleasantness are proposed for specific locations based on multiple linear regressions. A multilevel analysis was performed, and it is shown that a perceptual model that includes perceived loudness joined to the perceived time of presence of traffic, voices and birds explains 90% of the pleasantness variance due to the sound environment variations. Physical models that include the original acoustic indicators that are most correlated with perceptual variables explain 85% of this variance. Thanks to these models, a unique averaged pleasantness value is defined for each assessment location from the perceptual or physical collected assessments. The Pearson's correlation coefficient between the averaged perceived pleasantness and the modeled values from perceptual assessment reaches r(19)=0.98, and r(19)=0.97, with the modeled values from physical measurements. These results make it possible to consider the use of this kind of models in a cartographic context. As the path was traveled in both directions, the presentation-order effect has also been assessed, and it has been found that path direction did not have a significant impact on the pleasantness assessment at specific locations, except when very strong sound environment changes occurred. Finally, the study gives some insights about the retrospective global pleasantness assessment for urban walks. For very short walks between two assessment locations, a recency effect is shown. Nevertheless, this effect doesn't seem to be significant when longer routes are assessed.

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Many countries around the world have chosen lockdown and restrictions on people’s mobility as the main strategies to combat the COVID-19 pandemic. These actions have significantly affected environmental noise and modified urban soundscapes, opening up an unprecedented opportunity for research in the field. In order to enable these investigations to be carried out in a more harmonized and consistent manner, this paper makes a proposal for a set of indicators that will enable to address the challenge from a number of different approaches. It proposes a minimum set of basic energetic indicators, and the taxonomy that will allow their communication and reporting. In addition, an extended set of descriptors is outlined which better enables the application of more novel approaches to the evaluation of the effect of this new soundscape on people’s subjective perception.

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13Network-based sound monitoring systems are deployed in various cities over the world and mobile applications 14 allowing participatory sensing are now common. Nevertheless, the sparseness of the collected measurements, either 15 in space or in time, complicates the production of sound maps. This paper describes the results of a measurement 16campaign that has been conducted in order to test different spatial interpolation strategies for producing sound maps. 17Mobile measurements have been performed while walking multiple times in every street of the XIII'th district of