Urban noise reduction is a societal priority. In this context, the European Directive 2002/49/EC aims at producing strategic noise maps for large cities. However, nowadays the relevance of such maps is questionable, due to considerable uncertainties, which are rarely quantified. Conversely, the development of noise observatories can provide useful information for a more realistic description of the sound environment, but at the expense of insufficient spatial resolution and high costs. Thus, the CENSE project aims at proposing a new methodology for the production of more realistic noise maps, based on an assimilation of simulated and measured data, collected through a dense network of low-cost sensors that rely on new technologies. In addition, the proposed approach tries to take into account the various sources of uncertainty, either from measurements and modeling. Beyond the production of physical indicators, the project also includes advanced sound environments characterization, through sound recognition and perceptual assessments. CENSE is resolutely a multidisciplinary project, bringing together experts from environmental acoustics, data assimilation, statistics, GIS, sensor networks, signal processing, and noise perception. As the project is in launch state, the present communication will focus on a global overview, emphasizing the innovative and key points of the project.

International audienceBecause noise is a major pollution leading to non-negligible socio-economical impacts, many national regulations aim at reducing the population noise exposure. Within the context of the European directive 2002/49/EC, a special attention is paid to the evaluation of the existing noise environment. Nowadays, this assessment is addressed based on simulated noise maps, which however present some limitations due to the simplification of noise generation and propagation phenomena. Smartphone participative measurements are alternatively being developed, offering the high temporal and spatial granularities recommended by the EU directive. However, the existing approaches often lack a quantification of the produced noise maps accuracy, and are rarely user-oriented. In this context, within the framework of the EU project ENERGIC-OD, a Spatial Data Infrastructure (SDI “OnoMap”) has been developed to manage smartphones measurements using a dedicated Android application (“NoiseCapture”) and to produce relevant noise maps. In the present communication, this infrastructure is detailed, with a specific attention to the following major key points: data management and qualification from its production to its dissemination, use of standards for data interoperability, optimization of noise measurements, production of 24h noise maps based on measurements distributed over a day, integration of a confidence index on the produced data

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This study aims to produce dynamic noise maps based on a noise model and acoustic measurements. To do so, inverse modeling and joint state-parameter methods are proposed. These methods estimate the input parameters that optimize a given cost function calculated with the resulting noise map and the noise observations. The accuracy of these two methods is compared with a noise map generated with a meta-model and with a classical data assimilation method called best linear unbiased estimator. The accuracy of the data assimilation processes is evaluated using a “leave-one-out” cross-validation method. The most accurate noise map is generated computing a joint state-parameter estimation algorithm without a priori knowledge about traffic and weather and shows a reduction of approximately 26% in the root mean square error from 3.5 to 2.6 dB compared to the reference meta-model noise map with 16 microphones over an area of 3 km2.

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