Blind Calibration of Environmental Acoustics Measurements Using Smartphones.

Environmental noise control is a major health and social issue. Numerous environmental policies require local authorities to draw up noise maps to establish an inventory of the noise environment and then propose action plans to improve its quality. In general, these maps are produced using numerical simulations, which may not be sufficiently representative, for example, concerning the temporal dynamics of noise levels. Acoustic sensor measurements are also insufficient in terms of spatial coverage. More recently, an alternative approach has been proposed, consisting of using citizens as data producers by using smartphones as tools of geo-localized acoustic measurement. However, a lack of calibration of smartphones can generate a significant bias in the results obtained. Against the classical metrological principle that would aim to calibrate any sensor beforehand for physical measurement, some have proposed mass calibration procedures called "blind calibration". The method is based on the crossing of sensors in the same area at the same time, which are therefore supposed to observe the same phenomenon (i.e., measure the same value). The multiple crossings of a large number of sensors at the scale of a territory and the analysis of the relationships between sensors allow for the calibration of the set of sensors. In this article, we propose to adapt a blind calibration method to data from the NoiseCapture smartphone application. The method's behavior is then tested on NoiseCapture datasets for which information on the calibration values of some smartphones is already available.

Using a Clustering Method to Detect Spatial Events in a Smartphone-Based Crowd-Sourced Database for Environmental Noise Assessment.

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.

NoiseCapture smartphone application as pedagogical support for education and public awareness.

Teaching science subjects such as acoustics to youth or the general public can be facilitated by illustrating physical phenomena or scientific issues using fun experiences. A few years ago, our team developed a smartphone application named NoiseCapture with the aim of offering to anyone the opportunity to measure their sound environment and to share their geolocated measurements with the community in order to build a collective noise map. Since then, NoiseCapture team members have experimented with numerous interventions in schools or scientific events for the general public based on the app to explain not only societal and environmental issues related to noise but also to teach acoustic notions and to address technical and scientific topics associated with sound measurement. This paper describes some of the interventions implemented, in particular, in a school context through training courses given to middle school and university students, as well as teachers of secondary school, that focused on basic knowledge of buildings and environmental acoustics, on the practice of acoustic measurement, and on noise mapping. Some examples of interventions with the general public are also presented that were mostly integrated into scientific events.

A Smartphone-Based Crowd-Sourced Database for Environmental Noise Assessment.

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.

Inhomogeneous diffusion process in elongated rooms: An interpretation based on the dynamics of sound particles.

Previous studies showed that the reverberant field in elongated rooms is governed by non-homogeneous diffusion. The objective of this study is to physically interpret this phenomenon by considering the dynamics of the sound particles. Starting from the original diffusion theory, a quantity that can be interpreted as a "local" mean free path has been proposed and computed from the paths of the propagating particles. Based on the proportionality relationship between the mean free path and the diffusion coefficient, the spatial distribution of the latter could be estimated and successfully compared with a direct estimation using the Fick's law.

Low-Cost Sensors for Urban Noise Monitoring Networks-A Literature Review.

Noise pollution reduction in the environment is a major challenge from a societal and health point of view. To implement strategies to improve sound environments, experts need information on existing noise. The first source of information is based on the elaboration of noise maps using software, but with limitations on the realism of the maps obtained, due to numerous calculation assumptions. The second is based on the use of measured data, in particular through professional measurement observatories, but in limited numbers for practical and financial reasons. More recently, numerous technical developments, such as the miniaturization of electronic components, the accessibility of low-cost computing processors and the improved performance of electric batteries, have opened up new prospects for the deployment of low-cost sensor networks for the assessment of sound environments. Over the past fifteen years, the literature has presented numerous experiments in this field, ranging from proof of concept to operational implementation. The purpose of this article is firstly to review the literature, and secondly, to identify the expected technical characteristics of the sensors to address the problem of noise pollution assessment. Lastly, the article will also put forward the challenges that are needed to respond to a massive deployment of low-cost noise sensors.

Extending standard urban outdoor noise propagation models to complex geometries.

A hybrid method that combines a noise engineering method and the 2.5D boundary element method approximates outdoor sound propagation in large domains with complex objects more accurately than noise engineering methods alone and more efficiently than reference methods alone. Noise engineering methods (e.g., ISO 9613-2 or CNOSSOS-EU) efficiently approximate sound levels from roads, railways, and industrial sources in cities for simple, box-shaped geometries by first finding the propagation paths between the source and receiver, then applying attenuations (e.g., geometrical divergence and atmospheric absorption) to each path, and finally incoherently summing all of the path contributions. Standard engineering methods cannot model more complicated geometries but introducing an additional attenuation term quantifies the influence of complex objects. Calculating this extra attenuation term requires reference calculations but performing reference computations for each path is too computationally expensive. Thus, the extra attenuation term is linearly interpolated from a data table containing the corrections for many source/receiver positions and frequencies. The 2.5D boundary element method produces the levels for the real and simplified geometries and subtracting them yields a table of corrections. For a T-shaped barrier with two buildings, this approach reduces the mean error by approximately 2 dBA compared to a standard engineering method.

Collaborative noise data collected from smartphones.

Noise stands for an important human health and environmental issue. Indeed, noise causes annoyance and fatigue, interferes with communication and sleep, damages hearing and entails cardiovascular problems (WHO, 2011) [1]. From an environmental point of view, noise implies a lessening of both the richness and abundance of the animal species, an alteration of the communication, which can threaten the reproduction and predation, etc. (Newport et al., 2014; Shannon et al., 2014) [2], [3]. Consequently, effects related to environmental noise result in a huge cost for society, with 2.2 billion euros in France, for example, for the year 2013 (Bourges and Diel, 2015) [4]. In this context, the reduction of noise in the environment is a burning issue, which requires, firstly, carrying out an evaluation of noise in the environment, and secondly, to establish action plans to reduce noise annoyance. With the development of the concept of participatory measurement, and considering the extremely large number of people equipped with a smartphone while being "in mobility", the use of smartphones is potentially a relevant solution to realize a large-scale environmental noise evaluation. The data presented hereinafter are collected from the Android NoiseCapture application and shared from the OnoMap Spatial Data Infrastructure (SDI). The NoiseCapture approach consists in measuring noise along a path, and then to share data with the community. This approach has been developed within the framework of the European ENERGIC-OD project, which aims at deploying a set of Virtual Hubs (VH) to share heterogeneous data with third parties, in respect with the European INSPIRE, and at developing new and original services that can be useful for the community. The noise data that are acquired by volunteers around the world (citizen observations), are organized in three files, containing the path of measures (a set of points), standardized noise indicators, noise description and other useful variables (GPS accuracy, speed…). These data can be very relevant later to propose an environmental noise evaluation, through simple or complex treatments.

Including scattering within the room acoustics diffusion model: An analytical approach.

Over the last 20 years, a statistical acoustic model has been developed to predict the reverberant sound field in buildings. This model is based on the assumption that the propagation of the reverberant sound field follows a transport process and, as an approximation, a diffusion process that can be easily solved numerically. This model, initially designed and validated for rooms with purely diffuse reflections, is extended in the present study to mixed reflections, with a proportion of specular and diffuse reflections defined by a scattering coefficient. The proposed mathematical developments lead to an analytical expression of the diffusion constant that is a function of the scattering coefficient, but also on the absorption coefficient of the walls. The results obtained with this extended diffusion model are then compared with the classical diffusion model, as well as with a sound particles tracing approach considering mixed wall reflections. The comparison shows a good agreement for long rooms with uniform low absorption (α = 0.01) and uniform scattering. For a larger absorption (α = 0.1), the agreement is moderate, due to the fact that the proposed expression of the diffusion coefficient does not vary spatially. In addition, the proposed model is for now limited to uniform diffusion and should be extended in the future to more general cases.

Experimental analysis of the relationship between reverberant acoustic intensity and energy density inside long rooms.

In this paper, the validity of the Fick's law of diffusion in room acoustics is experimentally investigated inside long rooms. The room-acoustics diffusion model relies on Fick's law stating a proportionality relationship between sound intensity and energy density gradient inside a room through a constant diffusion coefficient. This relationship is investigated in the stationary state for the particular case of long rooms with different amounts of boundary scattering. Measurements were performed inside a 1:16 scale model, using a p-u sound intensity probe (calibrated with digital filters) to collect concurrent data in terms of sound pressure and axial velocity components. Then for each receiver position, sound intensity and energy density gradient were derived. The results show that inside long rooms the diffusion coefficient is not a constant but increases with the distance from the source with a slope depending on the scattering coefficient of the walls. Numerical simulations of the enclosures were performed too by using a sound particle-tracing code; a substantial agreement with the experimental findings is observed. The results imply that for such long enclosures, the diffusion model should consider a space-varying diffusion coefficient to be more consistent with real phenomena.

A numerical investigation of the Fick's law of diffusion in room acoustics.

In this paper the validity of the Fick's law of diffusion in room acoustics is investigated in the stationary state. The Fick's law, underlying the room-acoustics diffusion model, assumes a proportionality relationship between the local sound intensity and the energy density gradient, the proportionality constant being the so-called diffusion coefficient. This relationship, based on an analogy with the behavior of real particles in a scattering medium, is assessed by using a numerical tool simulating the actual dynamics of sound particles in a room. Two types of room geometries are considered: rooms with proportionate dimensions and long rooms. Concerning proportionate rooms the numerical analysis highlights the presence of weak variations of the reverberant energy density, generating an intensity vector pattern which is shown to be correctly described by the theoretical Fick's law and homogeneous diffusion. Conversely, inside long rooms, an estimate of the local value of the diffusion coefficient is carried out, showing that the reverberant sound field is well described by a spatially varying diffusion coefficient (non-homogeneous diffusion). The rate of increase of the estimated diffusion coefficient depends on the cross-sectional area of the room and on the boundaries absorption coefficient.

Introducing atmospheric attenuation within a diffusion model for room-acoustic predictions.

This paper presents an extension of a diffusion model for room acoustics to handle the atmospheric attenuation. This phenomenon is critical at high frequencies and in large rooms to obtain correct acoustic predictions. An additional term is introduced in the diffusion equation as well as in the diffusion constant, in order to take the atmospheric attenuation into account. The modified diffusion model is then compared with the statistical theory and a cone-tracing software. Three typical room-acoustic configurations are investigated: a proportionate room, a long room and a flat room. The modified diffusion model agrees well with the statistical theory (when applicable, as in proportionate rooms) and with the cone-tracing software, both in terms of sound pressure levels and reverberation times.

Modeling the sound transmission between rooms coupled through partition walls by using a diffusion model.

In this paper, a modification of the diffusion model for room acoustics is proposed to account for sound transmission between two rooms, a source room and an adjacent room, which are coupled through a partition wall. A system of two diffusion equations, one for each room, together with a set of two boundary conditions, one for the partition wall and one for the other walls of a room, is obtained and numerically solved. The modified diffusion model is validated by numerical comparisons with the statistical theory for several coupled-room configurations by varying the coupling area surface, the absorption coefficient of each room, and the volume of the adjacent room. An experimental comparison is also carried out for two coupled classrooms. The modified diffusion model results agree very well with both the statistical theory and the experimental data. The diffusion model can then be used as an alternative to the statistical theory, especially when the statistical theory is not applicable, that is, when the reverberant sound field is not diffuse. Moreover, the diffusion model allows the prediction of the spatial distribution of sound energy within each coupled room, while the statistical theory gives only one sound level for each room.

Sound-field modeling in architectural acoustics by a transport theory: application to street canyons.

The transport theory of sound particles is applied to the sound field modeling in architectural acoustics. A theoretical description is proposed for empty enclosures with complex boundary conditions, including both specular and diffuse reflections. As an example, the model is applied to street canyons. Therefore, an asymptotic approach is proposed to reduce the transport equation to a diffusion equation defined by only one parameter, the diffusion coefficient. This coefficient is a function of the reflection law of the building façades, the ratio of specular and diffuse reflections, as well as the street width. The model is then compared to Monte Carlo simulations of the propagation of sound particles in complex enclosures. As expected by the asymptotic approach, the model is in agreement with numerical results, but mainly for small street width and very diffuse reflections. Finally, a discussion is proposed in the conclusion, on the model's capabilities.