Analysis of Field Data to Describe the Effect of Context (Acoustic and Non-Acoustic Factors) on Urban Soundscapes

The need to improve acoustic environments in our cities has led to increased interest in correcting or minimising noise pollution in urban environments, something that has been associated with the resurgence of the soundscape approach. This line of research highlights the importance of context in the perception of acoustic environments. Despite this, few studies consider together a wide number of variables relating to the context, and analyse the relative importance of each. The purpose of this paper is therefore to identify the acoustic and non-acoustic characteristics of a place (context) that influence an individual’s perception of the sound environment and the relative importance of these factors in soundscape. The aim is to continue advancing in the definition of an acoustic comfort indicator for urban places. The data used here were collected in various soundscape campaigns carried out by Tecnalia in Bilbao (Spain) between 2011 and 2014. These studies involved 534 evaluations of 10 different places. The results indicate that many diverse contextual factors determine soundscape, the most important being the congruence between soundscape and landscape. The limitations of the findings and suggestions for further research are also discussed.The research presented in this manuscript has been developed with the financial support of the Basque Government, the Bilbao City council, and within the framework of the LIFE QUADMAP project (LIFE 10/ENV/IT/407

Moving towards for Active Role for Smart Grid Users: Study about the perception of smart grids among domestic consumers in Spain (UPGRID project) / Avanzando hacia el rol activo de los usuarios de las smart grids: estudio sobre la percepción de las redes eléctricas inteligentes entre los consumidores domésticos en España (proyecto UPGRID)

Integrating the perspective of smart grid users is the next challenge to be tackled in order to deploy fully the broad technical development of this new generation of electrical grids and improve the efficacy thereof. This is the conclusion reached by the social research developed within the framework of the European UPGRID project. This paper presents the main findings of the Spanish demonstration, working with domestic electricity consumers. The results indicate that these consumers know little about their contract and consumption of electricity, with regard to smart grids. In spite of this, there is some evidence that there is willingness among these consumers to make a change in their energy use towards more environmentally responsible behaviours, a tendency that needs to be developed so that consumers play an active role, which is essential in order to deliver optimal energy supply through smart grids. However, the results must be taken with a degree of caution, since, in spite of the major drive for recruitment, the sample was small and the experimental mortality between phases was high, so the research presented is exploratory in nature.We would like to thank the European Union for the funding it granted the UPGRID project ‘Real proven solutions to enable active demand and distributed generation flexible integration, through a fully controllable LOW Voltage and medium voltage distribution grid’ (H2020 Research and Innovation Programme, subsidy agreement number 646.531), the framework research project for this paper. We would also like to thank the different people and associations who collaborated with and participated in the social research conducted, without whom its development would not have been possible, and especially Iberdrola Distribución and EVE for providing us with the information required to conduct this research, and the Bilbao Federation of Neighbourhood Associations for their support and participation

NoisenseDB: An Urban Sound Event Database to Develop Neural Classification Systems for Noise-Monitoring Applications

The use of continuous monitoring systems to control aspects such as noise pollution has grown in recent years. The commercial monitoring systems used to date only provide information on noise levels but do not identify the noise sources that generate them. The identification of noise sources is an important aspect in order to apply corrective measures to mitigate the noise levels. In this sense, new technological advances like machine listening can enable the addition of other capabilities to sound monitoring systems such as the detection and classification of noise sources. Despite the increasing development of these systems, researchers have to face some shortcomings. The most frequent ones are on the one hand, the lack of data recorded in real environments and on the other hand, the need for automatic labelling of large volumes of data collected by working monitoring systems. In order to address these needs, in this paper, we present our own sound database recorded in an urban environment. Some baseline results for the database are provided using two original convolutional neural network based sound events classification systems. Additionally, a state of the art transformer-based audio classification system (AST) has been applied to obtain some baseline results. Furthermore, the database has been used for evaluating a semi-supervised strategy to train a classifier for automatic labelling that can be refined by human labellers afterwards