Infrasounds are very common in the natural environment. There are various opinions about their harmfulness or lack of harmfulness. One of the reasons of increasing interest in this issue is that there are more and more wind farms appearing close to building estates which are undoubtedly a source of infrasound. It is reasonable to present the results of research of infrasound noise connected not only with wind farms. In this study own results of research of infrasound noise related to daily human activity are presented. The measurements were carried out during housework, travel to the office or shop, and during shopping. The results are shown in the form of values of equivalent levels and 1/3-octave analyses. Taking into consideration the natural sources of infrasound in the environment, the measurements were conducted during both windy and windless weather. On the basis of the results of the measurements it was possible to define the daily exposure to infrasound noise. Those results were also compared with the available in the literature threshold values sensed by people. Estimated level of exposure to noise beyond workplace together with the level of exposure to noise at work enables to define daily exposure level, which means a better assessment of risk of health loss. Increasing social awareness of acoustic threat in everyday life allows us to identify the problem and at the same time improve the quality of rest and efficiency at work.

A significant threat to critical infrastructure of computer systems has a destructive impact caused by infrasound waves. It is shown that the known infrasound generations are based on using the following devices: a Helmholtz Resonator, Generation by using a Pulsating Sphere such as Monopolies, Rotor-type Radiator, Resonating Cylinder, VLF Speaker, Method of Paired Ultrasound Radiator, and airscrew. Research of these devices was made in this paper by revealing their characteristics, main advantages and disadvantages. A directional pattern of infrasound radiation and a graph of dependence of infrasound radiation from the consumed power was constructed. Also, during the analysis of these devices, there was proven a set of basic parameters, the values of which make it possible to characterize their structural and operational characteristics. Then approximate values of the proposed parameters of each those considered devices, were calculated. A new method was developed for evaluating the effectiveness of infrasound generation devices based on the definition of the integral efficiency index, which is calculated using the designed parameters. An example of practical application of the derived method, was shown. The use of the method makes it possible, taking into account the conditions and requirements of the infrasound generation devices construction, to choose from them the most efficient one.

Complaints and awareness about environmental low-frequency (LF) noise and infrasound (IS) have increased in recent years, but knowledge about perceptual mechanisms is limited. To evaluate the use of the brain’s frequency-following response (FFR) as an objective correlate of individual sensitivity to IS and LF, we recorded the FFR to monaurally presented IS (11 Hz) and LF (38 Hz) tones over a 30-phon range for 11 subjects. It was found that 11-Hz FFRs were often significant already at ~0 phon, steeply grew to 20 phon, and saturated above. In contrast, the 38-Hz FFR growth was relatively shallow and continued to 60 phon. Furthermore, at the same loudness level (30 phon), the 11-Hz FFR strength was significantly larger (4.5 dB) than for 38 Hz, possibly reflecting a higher phase synchronization across the auditory pathway. Overall, unexpected inter-individual variability as well as qualitative differences between the measured FFR growth functions and typical loudness growth make interpretation of the FFR as objective correlate of IS and LF sensitivity difficult.

Infrasound signal classification is vital in geological hazard monitoring systems. The traditional classification approach extracts the features and classifies the infrasound events. However, due to the manual feature extraction, its classification performance is not satisfactory. To deal with this problem, this paper presents a classification model based on variational mode decomposition (VMD) and convolutional neural network (CNN). Firstly, the infrasound signal is processed by VMD to eliminate the noise. Then fast Fourier transform (FFT) is applied to convert the reconstructed signal into a frequency domain image. Finally, a CNN model is established to automatically extract the features and classify the infrasound signals. The experimental results show that the classification accuracy of the proposed classification model is higher than the other model by nearly 5%. Therefore, the proposed approach has excellent robustness under noisy environments and huge potential in geophysical monitoring.

A diagnostic technique based on independent component analysis (ICA), fast Fourier transform (FFT), and support vector machine (SVM) is suggested for effectively extracting signal features in infrasound signal monitoring. Firstly, ICA is proposed to separate the source signals of mixed infrasound sources. Secondly, FFT is used to obtain the feature vectors of infrasound signals. Finally, SVM is used to classify the extracted feature vectors. The approach integrates the advantages of ICA in signal separation and FFT to extract the feature vectors. An experiment is conducted to verify the benefits of the proposed approach. The experiment results demonstrate that the classification accuracy is above 98.52% and the run time is only 2.1 seconds. Therefore, the proposed strategy is beneficial in enhancing geophysical monitoring performance.