1. INTRODUCTION Due to the progress in microprocessor and digital signal processing technologies , modern noise monitoring equipment offers increased performance. Current " sound-level meters " provide much more sophisticated measurements than old-fashioned sound-levels, like statistical and spectral analysis, or noise event recording. Moreover, there is actually a trend toward integrating noise monitoring systems (NMS) with personal computers to add further processing and memory capabilities. If this increased sophistication is generally welcomed, it has also some drawbacks. The noise control expert is provided with an ever growing wealth of information, and extracting the relevant features from the data becomes more and more difficult. Consequently, there are current research interests in the development of " intelligent " noise monitoring equipments, in order to simplify and automate as much as possible the data analysis task. One current research thread concerns the automatic recognition of environmental noise sources. In automatic recognition of environmental noise sources, the goal is to classify a noise event based on its acoustic signature [1]. That is, it is expected that the NMS will provide, in addition to the level and time of occurrence of a noise event, some information on the nature of its source (e.g., train passing by, airplane flying over,.. .). Such noise recognition capabilities can be obtained by adding a sound recognition subsystem to a classical NMS. Various classification approaches have been considered for the realization of this subsystem, including neural networks, statistical classi-fiers, and ad-hoc methods. Preliminary studies have shown the feasibility of automatic noise recognition [2][3][4]. In these studies, standard pattern classification methods were applied straightforwardly without reference to the specificities of the noise recognition problem or to the environmental acoustics framework in which it takes place. We believe that utilization of acoustical-domain knowledge in an environmental noise recognition would be beneficial. Therefore, in this paper, we try to answer the following question , " what are the desirable properties of an automatic noise recognition system? " from the point of view of the noise control practician. A method for implementing such properties in a practical system is then suggested.
In situ evaluation of the effectiveness of noise barriers may be based on the assessment of their intrinsic or extrinsic characteristics. The evaluation of intrinsic characteristics is based on acoustic properties, such as noise barrier absorption or insulation. The evaluation of the extrinsic characteristics is based on the calculation of the barrier Insertion Loss, which is defined as the difference in the noise level before and after the installation of the barrier. Insertion Loss is calculated using two different approaches: the direct and indirect methods. The direct method is used when the barrier has not been installed yet or can be removed, while the indirect method is used when the barrier is already installed and cannot be easily removed. This chapter describes the different approaches used in the scientific literature for in situ evaluation of the effectiveness of noise barriers and discusses the noise attenuation levels obtained with each approach.
The goal of our research is developing an evacuation guidance system that emits sounds on a set of loudspeakers in a spatial sequence to achieve rapid evacuation in emergency situations. For this goal, we evaluate the auditory recognition properties of emitting sounds and their ability to make evacuees follow the movement of sound stimuli in this chapter. We conduct three experiments to assess the proposed evacuation guidance method. The first and the second ones are done to investigate the recognition properties of the positions and directions of the emitting sound stimuli. The third one is done to evaluate the ability to guide evacuees to exit using the emitting sound in the spatial sequence. In the first experiment, we consider whether the four factors related to the sound-emitting method affect the identification of the emitting sound stimuli. Additionally, we investigate the patterns of emitted sounds that can easily recognize their position and direction. In the third experiment, we consider whether the evacuee can follow the emitting sound on a set of loudspeakers in spatial sequence. Moreover, it is discussed that the proposed guidance system provided a more detailed evacuation route for evacuees.
Noise control refers to a set of methods, techniques, and technologies that allows obtaining acceptable noise levels in a given place, according to economic and operational considerations. The question of “acceptance” is for what or for whom. Generally, there is no single answer to this question, nor is there a single solution to any given problem, as long as regulatory compliance is achieved. Noise control does not necessarily imply the reduction of noise emissions—it refers to making acceptable sound pressure levels of immission (i.e., the signal reaching the receiver). This chapter aims to present the basis of noise control techniques, both in emission and propagation, to finally achieve the most current protection techniques for the receivers, when there are no more alternatives in the previous steps.
According to the European Law, noise maps in cities have to be worked out and updated every 5 years. Because of this, it is interesting to establish new methodologies to develop and update the noise maps in a more efficient way. Although there are specific standards to carry out noise maps and a good practice guide was defined, there is not a common procedure in the definition of the noise map. In each research, a specific methodology is defined based on the experience of the researchers and the characteristics of the town. In this work, a methodology based on a street typology classification is proposed to be applied to noise maps. This methodology allows allocation of the mean power and the temporal behavior to each street from its characteristics and the time profiles measured with semi-permanent noise monitoring systems. The methodology was developed, tested, and validated in the city of Cuenca (Spain) and the results obtained are shown in this chapter.
Activities such as development of industrialisation, urbanisation is a part of our life in the present scenario. During this phase we face a lot of health issues due to noise pollution. Growing of vehicle traffic is one of the major causes towards noise pollution and it affects significantly on the environment. The impact of such pollution had been assessed in 20 major squares (Commercial, residential and silence area) of the Balasore town during and after lockdown imposition of Covid-19. During lockdown period, the noise level of the town was within the permissible limit set by CPCB while before and after lockdown period it was beyond the permissible limit. The demographics and psychophysiological (annoyance, sleeping problem, tiredness, headache, and depression) responses of the participants were collected using standard questionnaires. It was also observed that there were better health conditions among the public (150 participated in the questionnaire) during the lockdown period, then before and after the lockdown phase. It was revealed that socio-demographic factors have no effects on the annoyance level.
Free Access Description of Figures Book Editor(s):Shahram Taherzadeh, Shahram TaherzadehSearch for more papers by this author First published: 14 March 2014 https://doi.org/10.1002/9781118863848.dedicat AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onFacebookTwitterLinked InRedditWechat Noise Control RelatedInformation
