Noise is a major source of pollution with a strong impact on health. Noise assessment is therefore a very important issue to reduce its impact on humans. To overcome the limitations of the classical method of noise assessment (such as simulation tools or noise observatories), alternative approaches have been developed, among which is collaborative noise measurement via a smartphone. Following this approach, the NoiseCapture application was proposed, in an open science framework, providing free access to a considerable amount of information and offering interesting perspectives of spatial and temporal noise analysis for the scientific community. After more than 3 years of operation, the amount of collected data is considerable. Its exploitation for a sound environment analysis, however, requires one to consider the intrinsic limits of each collected information, defined, for example, by the very nature of the data, the measurement protocol, the technical performance of the smartphone, the absence of calibration, the presence of anomalies in the collected data, etc. The purpose of this article is thus to provide enough information, in terms of quality, consistency, and completeness of the data, so that everyone can exploit the database, in full control.

Noise has become a very notable source of pollution with major impacts on health, especially in urban areas. To reduce these impacts, proper evaluation of noise is very important, for example by using noise mapping tools. The Noise-Planet project seeks to develop such tools in an open science platform, with a key open-source smartphone tool “NoiseCapture” that allows users to measure and share the noise environment as an alternative to classical methods, such as simulation tools and noise observatories, which have limitations. As an alternative solution, smartphones can be used to create a low-cost network of sensors to collect the necessary data to generate a noise map. Nevertheless, this data may suffer from problems, such as a lack of calibration or a bad location, which lowers its quality. Therefore, quality control is very crucial to enhance the data analysis and the relevance of the noise maps. Most quality control methods require a reference database to train the models. In the context of NC, this reference data can be produced during specifically organized events (NC party), during which contributors are specifically trained to collect measurements. Nevertheless, these data are not sufficient in number to create a big enough reference database, and it is still necessary to complete them. Other communities around the world use NC, and one may want to integrate the data they collected into the learning database. In order to achieve this, one must detect these data within the mass of available data. As these events are generally characterized by a higher density of measurements in space and time, in this paper we propose to apply a classical clustering method, called DBSCAN, to identify them in the NC database. We first tested this method on the existing NC party, then applied it on a global scale. Depending on the DBSCAN parameters, many clusters are thus detected, with different typologies.

Environmental noise is a major source of annoyance with serious effects on health. Therefore, noise assessment is crucial to reduce these impacts. An alternative approach has been developed (i.e. noise measurement with smartphones) to overcome the limitations of classical assessment methods (e.g. simulation tools or noise observatories). In this way, the NoiseCapture application consists of measuring and sharing data, in order to produce community noise maps. Nevertheless, collected data may suffer from problems such as a lack of calibration, which lowers its quality. Quality control is therefore very important to enhance the data analysis and the relevance of the noise maps. Having trustworthy data as a reference can help in assessing the database, for example using machine-learning methods. WIth NoiseCapture, such data can be collected thanks to a NoiseCapture Party, an organized event, on limited space/time (i.e. a cluster of data). Because not all events are known by the people in charge of NoiseCapture, and since the corresponding data can be considered of better quality, so their detection is a relevant task to increase the trust database. In the present communication, a clustering methodology is then proposed to automatically detect data that could be produced in such events.