Smart cities are required to engage with local communities by promoting a user-centred approach to deal with urban life issues and ultimately enhance people’s quality of life. Soundscape promotes a similar approach, based on individuals’ perception of acoustic environments. This paper aims to establish a model to implement soundscape maps for the monitoring and management of the acoustic environment and to demonstrate its feasibility. The final objective of the model is to generate visual maps related to perceptual attributes (e.g. ‘calm’, ‘pleasant’), starting from audio recordings of everyday acoustic environments. The proposed model relies on three main stages: (1) sound sources recognition and profiling, (2) prediction of the soundscape’s perceptual attributes and (3) implementation of soundscape maps. This research particularly explores the two latter phases, for which a set of sub-processes and methods is proposed and discussed. An accuracy analysiswas performed with satisfactory results: the prediction models of the second stage explained up to the 57.5% of the attributes’ variance; the cross-validation errors of the model were close to zero. These findings show that the proposed model is likely to produce representative maps of an individual’s sonic perception in a given environment.

Article Soundscape mapping in environmental noise management and urban planning: case studies in two UK cities was published on December 20, 2017 in the journal Noise Mapping (volume 4, issue 1).

This paper contributes on the popular topic of smartphone-based noise mapping and in general participatory sensing. It has a dual aim as on one hand it provides a methodological framework on the spatio-temporal variability of noise and soundscape and in parallel, it investigates the role of green space and water features on soundscape preference with respect to certain activities. The study was organised in Zernike University Campus in Groningen, the Netherlands around 13 predefined locations, equipped with seating facilities. A big part of the study area was recently redesigned as part of the Zernike promenade. Students, enrolled in the course of Environment and Engineering, used the ArcGIS 123 Survey and the NoiseCapture mobile applications for soundscape and noise data collection respectively. Statistical analysis and GIS tools were used to investigate the noise and soundscape variability in different time periods from morning to evening. Additionally, the effect of green space and water features on soundscape quality and users' preference is identified by exploring the links with activities related to socializing and relaxing. The contribution of the above-mentioned technologies in participatory planning and urban design interventions is discussed parallel to the educational benefit of these tools in the achievement of the course learning objectives.