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.

The efficiency of noise barriers largely depends on their geometry. In this paper, the performance of noise barriers was simulated using the numerical Boundary Element Method (BEM). Traffic noise was particularly considered with its standardized noise spectrum adapted to human hearing. The cross-section of the barriers was varied with the goal of finding the optimum shape in comparison to classical rectangular barriers. The barrier performance was calculated at different receiver points for a fixed barrier height and source position. The magnitude of the insertion loss parameter was used to evaluate the performance change, both in one-third octave bands and as the broadband mean insertion loss value. The proposed barriers of varying cross-section were also compared with a typical T-shape barrier of the same height.