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.

This paper presents a detailed analysis of quadcopter noise emitted in different flight regimes and suggests an innovative approach to quadcopter sound detection. Currently, quadcopters have become extremely popular and are used for various applications. In some case scenarios, these applications are not legal; therefore, they represent a threat, and early detection of quadcopters becomes imperative. However, conventional detection with radars and infrared cameras does not provide satisfactory results, since some quadcopters have relatively small dimensions, and their motors have low heat emissions. Thus, this paper focuses on the noise emitted by quadcopters, which is connected to the changing revolutions per minute of the electric quadcopter motors. Thus, the paper connects the unique quadcopter features with the emitted sounds, which can be used for detection in different acoustic environments. The suggested algorithm uses signal processing with a nonlinear function, which results in new spectral lines that correspond to rotational speed differences between the front and rear motors. The analysis shows that the processed signal mixed with a traffic noise signal can be detected when the level reaches 3 dB lower than the level of the surrounding noise.

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.

Nowadays, each individual is exposed to noise on a daily basis, and noise is often referred as in literature as a plague of modern society. Noise pollution is often overlooked when compared to other environmental pollutions (e.g. air, water, soil pollution). However, same as the all aforementioned pollutions, noise exposure has an accumulating character, meaning that the harmful effect of noise is detected only after a long period of time. Long exposure to noise pollution can be displayed as a bad mood, fatigue, insomnia, headache and loss of concentration, which causes reduced work ability and ultimately permanent hearing impairment. The goal of this chapter is to present two different approaches (traditional and contemporary) in noise reductions. The aim of both approaches is to link objective and subjective acoustic parameters, in order to plan future urban infrastructures while keeping in mind the existing acoustic environments, and to create and implement new solutions that will design, preserve and improve acoustic environments. Thus, we can conclude this chapter will be oriented towards human health and overall quality of life in terms of noise reduction.

The goal of this paper is to design a broadband acoustic camera using micro-electromechanical system (MEMS) microphones. The paper describes how an optimization of the microphone array has been carried out. Furthermore, the final goal of the described optimization is that the gain in the desired direction and the attenuation of side lobes is maximized at a frequency up to 4 kHz. Throughout the research, various shapes of microphone arrays and their directivity patterns have been considered and analyzed using newly developed algorithms implemented in Matlab. A hemisphere algorithm, genetic algorithm, and genetic square algorithm were used to find the optimal position and number of microphones placed on an acoustic camera. The proposed acoustic camera design uses a large number of microphones for high directional selectivity, while a field programmable gate array system on a chip (FPGA SoC) is selected as the processing element of the system. According to the obtained results, three different acoustic camera prototypes were developed. This paper presents simulations of their characteristics, compares the obtained measurements, and discusses the positive and negative sides of each acoustic camera prototype.

Combined acoustical and economical noise barrier optimization using genetic algorithms This paper studies noise barrier optimization using the Boundary Element Method (BEM) as a numerical technique and Genetic Algorithms (GA). Noise barriers are optimised according to acoustical, technological and economical properties so as to obtain an optimum noise barrier. In order to optimise acoustical and economical properties of noise barriers, the use is made of a genetic algorithm that forms a noise barrier out of given shapes. A new noise barrier evaluation parameter, named the noise barrier cost parameter (K e), is also defined in the paper. Using the genetic algorithm and the noise barrier cost parameter (K e), it is easy to create, develop and construct an appropriate noise barrier.