Currently, noise pollution is a major problem especially in urban areas, and moreover traffic noise is the most significant source of noise in cities. A large number of cars and other road vehicles that have internal combustion engines are making road traffic noise a leading noise pollution source. Electric and hybrid cars, which are nowadays slowly replacing them, give rise to lower noise level in urban areas as their engines are generally silent. However, the mere absence of internal combustion engines cannot be the only measure for lowering noise levels in urban areas. The goal of this chapter is to define and describe traffic noise, the reasons for its occurrence, and all existing ways of reducing traffic noise.
This paper deals with the problem of traffic noise in urban areas in terms of noise mapping. It explains in detail the Mobile Crowdsensing (MCS) method and, furthermore, compares the results obtained with this method with the results gained from the standard method that uses a sound level metre. The research done in this paper shows that the MCS method can make noise mapping easier, cheaper and less time-consuming in terms of creating representative noise maps developed on measurements but also noise maps developed on calculations and simulations. The main idea is to show that accuracy and precision of measurements obtained by using calibrated smartphones are acceptable. The paper suggests that when using the smartphone measurement application, the calibration of the measurement chain can be done in free field with class 1 sound level metre, and noise map can be checked in a much larger number of points (in comparison with the standard measurement method) and therefore, smartphones can be used as instruments for creating or even checking final noise maps in urban environment. Another advantage of this method is that citizens can engage in noise monitoring in urban areas and become aware of the noise pollution in their cities. ARTICLE HISTORY
This work was supported in part by the European Union from the European Regional Development Fund (ERDF) under Project KK.01.2.1.01.0103 4D Acoustical Camera (in Croatian: 4D Akustička kamera).
This paper describes a student project of building an impedance tube for measuring the absorption coefficient using the transfer-function method, in accordance with the standard ISO 10534-2. This method is well-established and has many advantages compared to the older method using standing wave ratio (ISO 10354-1) in terms of measurement speed and accuracy. For the tube, only inexpensive materials and transducers were used. The tube was designed for the frequency range between 90 and 2000 Hz. In order to achieve this range with one tube, three microphone positions have been used. The resulting absorption coefficient has been calculated using the one- and two-microphone method. Different broadband excitation signals have been used in order to compare their robustness, such as MLS, frequency sweep and white noise. Various problems with the design and construction are addressed and the optimal configuration is discussed.
This paper investigates different approaches in designing an acoustic camera with respect to the shape of the camera as well as the number of microphones and their position on the camera. Micro electro-mechanical systems (MEMS) microphones are used in this research for the purpose of designing an acoustic camera. Several simulations implemented in MATLAB were performed for square MEMS microphone arrays, bearing in mind our primary goal, which is to design a broadband frequency range acoustic camera with MEMS microphones. In addition, a microphone array in the shape of a hemisphere was designed in order to compare all of the obtained results. Results gathered in the simulations have shown that using the square arrays and a hemispherical array enables us to construct four different broadband frequency range acoustic cameras. All of the considered versions of an acoustic camera have a respectable gain in the desired direction (i.e. the gain of the main lobe) and, in addition, a significant attenuation of side lobes. Keeping in mind the aforementioned requirements (i.e. the main lobe gain and attenuation of side lobes) it can be concluded that, from all of the considered designs, the best design is the acoustic camera with 24 MEMS microphone square array.
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