Actual Noise Research Articles

No Effect of Forest Representations on State Anxiety, Actual and Perceived Noise

Abstract Previous research indicates that nature and nature representations might have positive effects and noise negative effects on various facets of life, such as performance, perceived life quality, and physical and mental health. In this intervention, we observed whether posters showing a representation of nature (forests) can be used to reduce actual noise, perceived noise, and state anxiety in university library users. Measurements were taken twice daily for a 5-day period pre-intervention (before posters were installed) and again during the intervention, when posters were installed. No significant differences were found for perceived or actual noise levels or for self-report state anxiety levels between pre-intervention and intervention phase. Correlations between actual and perceived noise, and actual noise and state anxiety, were small in their magnitude and non-significant, with the exception of state anxiety and perceived noise during the intervention phase, suggesting a weak positive relationship. Finally, in hierarchical linear regression models, actual and perceived (overall and talking) noise and intervention phase were non-significant predictors of state anxiety. Small effect sizes of nature representations on state anxiety, as well as actual and perceived noise, suggest posters of forests to not be an effective intervention for anxiety and actual and perceived noise reduction in a university library.

Two combination methods of piecewise unsaturated tri-stable stochastic resonance system and bearing fault detection under different noise

In this paper, a piecewise unsaturated tri-stable stochastic resonance (PUTSR) system is proposed to address the issue of output saturation. Firstly, the unsaturated characteristics of the PUTSR system are verified, followed by a discussion on the system performance of PUTSR under two different combinations of coupled and cascaded systems. The two-dimensional coupled piecewise unsaturated tri-stable stochastic resonance (TCPUTSR) system and cascaded piecewise unsaturated tri-stable stochastic resonance (CPUTSR) system are proposed. By implementing the adiabatic approximation theory, the steady-state probability density (SPD) of TCPUTSR system and spectral amplification (SA) coefficient of TCPUTSR and CPUTSR systems are derived respectively. The impact of different system parameters on the SPD and SA are also investigated. Finally, PUTSR, TCPUTSR and CPUTSR systems are applied to detection on the fault signal of bearing. Gaussian white noise and color noise are used to simulate actual noise environments in the experiments, while the adaptive genetic algorithm (AGA) is used to optimize the parameters and measure the system performance via signal-to-noise ratio improvement (SNRI). Results indicate that both coupled and cascaded systems demonstrate better detection capabilities than individual systems, with the CPUTSR system offering better enhancement and detection of bearing fault signals. Under Gaussian white noise, the SNRI of the CPUTSR system is 1.449 dB ~ 3.198 dB higher than that of the PUTSR and TCPUTSR systems, while under color noise it is 1.566 dB ~ 2.849 dB higher. This paper mainly compares the spectral amplification capability and fault signal detection capability of the same system under two different combinations of coupled and cascaded. The theoretical and numerical simulations further verify that the designed cascaded system outperforms the coupled system and proves the great superiority of CPUTSR system. These findings provide crucial theoretical support and practical application prospects for engineering purposes.

Real-Time Detection of Intruders Using an Acoustic Sensor and Internet-of-Things Computing.

Modern home automation systems include features that enhance security, such as cameras and radars. This paper proposes an innovative home security system that can detect burglars by analyzing acoustic signals and instantly notifying the authorized person(s). The system architecture incorporates the concept of the Internet of Things (IoT), resulting in a network and a user-friendly system. The proposed system uses an adaptive detection algorithm, namely the "short-time-average through long-time-average" algorithm. The proposed algorithm is implemented by an IoT device (Arduino Duo) to detect people's acoustical activities for the purpose of home/office security. The performance of the proposed system is evaluated using 10 acoustic signals representing actual events and background noise. The acoustic signals were generated by the sounds of keys shaking, the falling of a small object, the shrinking of a plastic bag, speaking, footsteps, etc. The effects of different algorithms' parameters on the performance of the proposed system have been thoroughly investigated.

Underwater Noise Modeling and Its Application in Noise Classification with Small-Sized Samples

Underwater noise classification is of great significance for identifying ships as well as other vehicles. Moreover, it is helpful in ensuring a marine habitat-friendly, noise-free ocean environment. But a challenge we are facing is the small-sized underwater noise samples. Because noise is influenced by multiple sources, it is often difficult to determine and label which source or which two sources are dominant. At present, research to solve the problem is focused on noise image processing or advanced computer technology without starting with the noise generation mechanism and modeling. Here, a typical underwater noise generation model (UNGM) is established to augment noise samples. It is established by generating noise with certain kurtosis according to the spectral and statistical characteristics of the actual noise and filter design. In addition, an underwater noise classification model is developed based on UNGM and convolutional neural networks (CNN). Then the UNGM-CNN-based model is used to classify nine types of typical underwater noise, with either the 1/3 octave noise spectrum level (NSL) or power spectral density (PSD) as the input features. The results show that it is effective in improving classification accuracy. Specifically, it increases the classification accuracy by 1.59%, from 98.27% to 99.86%, and by 2.44%, from 97.45% to 99.89%, when the NSL and PSD are used as the input features, respectively. Additionally, the UNGM-CNN-based method appreciably improves macro-precision and macro-recall by approximately 0.87% and 0.83%, respectively, compared to the CNN-based method. These results demonstrate the effectiveness of the UNGM established in noise classification with small-sized samples.

Impact of quasi-periodic and steep-spectrum timing noise on the measurement of pulsar timing parameters

Abstract Timing noise in pulsars is often modelled with a Fourier-basis Gaussian process that follows a power law with periodic boundary conditions on the observation time, Tspan. However the actual noise processes can extend well below 1/Tspan, and many pulsars are known to exhibit quasi-periodic timing noise. In this paper we investigate several adaptions that try to account for these differences between the observed behaviour and the simple power-law model. Firstly, we propose to include an additional term that models the quasi-periodic spin-down variations known to be present in many pulsars. Secondly, we show that a Fourier basis of 1/2Tspan can be more suited for estimating long term timing parameters such as the spin frequency second derivative (F2), and is required when the exponent of the power spectrum is greater than ∼4. We also implement a Bayesian version of the generalised least squares ‘Cholesky’ method which has different limitations at low frequency, but find that there is little advantage over Fourier-basis methods. We apply our quasi-periodic spin down model to a sample of pulsars with known spin-down variations and show that this improves parameter estimation of F2 and proper motion for the most pathological cases, but in general the results are consistent with a power-law model. The models are all made available through the run_enterprise software package.

2D Inversion of DC Resistivity Method to Detect High-resistivity Targets inside Dams

As the first barrier of flood control, the integrity and continuity of dams are of the utmost importance. However, with the influence of natural weathering, running water erosion, and humanistic factors, various degrees of damage are prone to occur inside and outside dams. Therefore, it is necessary to detect the integrity of dams and evaluate their flood control capacity. Different from the previous detection of water-filled imperfect areas, we used numerical simulation and measured data analysis and considered the influence of actual noise to conduct DC resistivity detection and 2D inversion research on the high-resistivity imperfect areas existing in dams. The research results show that both the calculated apparent resistivity and the resistivity obtained by 2D inversion deviate from the real resistivity of the dam. A dam with a height less than 5 m has a relatively small influence on the 2D inversion results of the DC resistivity method, and the inversion results can accurately show the spatial information of the high-resistivity imperfect areas. The distance between the river and dam has a major influence on the inversion results, but the influence on the inversion results can be ignored when the distance is greater than 20 m in the model of this paper. The 2D inversion of the DC resistivity method can be used to detect high-resistivity imperfect areas of dams, and the 2D spatial information of the 3D geological bodies can be accurately obtained.

Requirements and best practices for traffic noise model validation

The exercise of validating a predictive model typically consists of modeling an existing real-world condition and predicting some resulting quantity of interest, and then comparing the predicted results to actual measured values for the same condition within a specified margin of error. In the case of road traffic noise models this would include modeling the characteristics of the existing roadway condition (geometry of the roadway network; number, vehicle type and speed of vehicles using the roadways; pavement types; intervening terrain; etc.) and comparing the resulting predicted noise level to actual noise measurements for the same conditions. In this paper the authors will provide a concise background of the validation purpose and process as well as a review of agency requirements and best practices for traffic noise model validation procedures.

Deep learning for denoising High-Rate Global Navigation Satellite System data

High-rate global navigation satellite system (HR-GNSS) data records ground displacements and can be used to identify earthquakes and slow slip events. One limitation of such data is the high amplitude, cm-level noise which make it difficult to identify processes that produce surface displacements smaller than these values. Deep learning has proven adept at performing many useful tasks in seismology and geophysics. Here we explore using deep learning to denoise HR-GNSS data. We develop three different convolutional neural networks with similar architectures but different targets. Training data are synthetic HR-GNSS records and actual noise recordings that are superimposed to generate noisy signals. We train each of the three models to output masks that can be used to reconstruct the true signal. We use a set of performance metrics that quantify the models’ ability to denoise the testing data and find that denoising significantly improves the signal-to-noise ratio and the ability to identify first arrivals. Finally, we test the models on HR-GNSS records from the Ridgecrest earthquakes recorded at stations that have nearly colocated strong-motion sites used ground-truth the denoising results. We find that the models greatly improve the signal-to-noise ratios in these records and make the P-wave onset clearly identifiable.

Multi-Mode Imaging of Ambient Background Noise for Karst Detection in the Limestone Area Based on Frequency-Bessel Transform

In response to the challenges of karst geophysical exploration in an environment with strong external interference, this paper proposes a new method, namely the frequency-Bessel transform method, for extracting multi-order dispersion curves of surface waves from background noise to characterize karst. The observation noise data of the Wuhan karst development area are used as an example, where the dolomitic limestone and limestone mixed with dolomite of the Jialing River Formation of the middle lower Triassic are widely developed in the observation area. The frequency-Bessel transform method involves performing a Bessel integral transformation on the cross-correlation coefficient of background noise in the frequency domain. Firstly, by synthesizing theoretical noise data and comparing it with the spatial autocorrelation method—which is currently the main method for extracting the fundamental dispersion curve of surface waves—it is verified that the frequency-Bessel transform method can extract the higher-mode dispersion curve. Then, by taking the actual measured single-point noise data as an example, the effect of applying the frequency-Bessel transform to the actual noise data is tested, and the inversion of the fine structure of the strata by the addition of higher-mode dispersion, the use of the damped least squares inversion method, and the joint inversion of fundamental and higher-mode dispersion curves are analyzed. The higher-mode dispersion curve of Rayleigh surface wave extracted by the frequency-Bessel transform is much clearer, and the 2D shear wave velocity structure profile obtained from inversion explains the karst development area, karst strip area, and thickness of the Quaternary overburden. The inferred results match with the actual borehole data. Multi-mode imaging of background noise based on the frequency-Bessel method can be applied to depict karst in complex backgrounds, and has significant potentiality in the field of ambient seismic noise tomography, providing a new idea and method for karst detection in near-surface engineering.

Relation between the noise correlations and the spin structure factor for Mott-insulating states in SU(N) Hubbard models

It is well established that the noise correlations measured by time-of-flight imaging in cold-atom experiments, which correspond to the density-density correlations in the momentum space of trapped atomic gases, can probe the spin structure factor deep in the Mott-insulating regime of SU(2) Hubbard models. We explicitly derive the mathematical relation between the noise correlations and the spin structure factor in the strong-interaction limit of SU$(N)$ Hubbard models at any integer filling $\rho$. By calculating the ground states of one-dimensional SU$(N)$ Fermi-Hubbard models for $2\leq N\leq 6$ with use of the density-matrix renormalization-group method, we confirm the relation numerically in the regime of strong interactions $U \gg t$, where $U$ and $t$ denote the onsite interaction and the hopping energy. We show that the deviation between the actual noise correlations and those obtained from the spin structure factor scales as approximately $(t/U)^2$ for $\rho=1$ at intermediate and large lattice sizes on the basis of numeric and semi-analytic arguments.

Learning vehicle trajectory uncertainty

A novel approach for vehicle tracking using a hybrid adaptive Kalman filter is proposed. The filter utilizes recurrent neural networks to learn the vehicle’s geometrical and kinematic features, which are then used in a supervised learning model to determine the actual process noise covariance in the Kalman framework. This approach addresses the limitations of traditional linear Kalman filters, which can suffer from degraded performance due to uncertainty in the vehicle kinematic trajectory modeling. Our method is evaluated and compared to other adaptive filters using the Oxford RobotCar dataset, and has shown to be effective in accurately determining the process noise covariance in real-time scenarios. Overall, this approach can be implemented in other estimation problems to improve performance.

Real-Time Construction Site Noise Mapping System Based on Spatial Interpolation

Because construction noise is one of the major factors affecting the health and safety of workers and nearby residents, it must be managed appropriately. Thus, this study aimed to map real-time noise information generated during construction using a sensor network and spatial interpolation. An optimal sensor was selected in terms of cost and performance by comparing noise measurements with an officially certified sensor in a semianechoic chamber. Field constraints that required or avoided placing the sensors were identified to secure satisfactory noise-estimation performance without interfering with fieldwork. A noise-estimation model was developed based on spatial interpolation, and its performance was evaluated in an experimental environment. The experiment was conducted in two different cases: the first case with only one noise source and the second case by adding another extra noise source. For each case, the optimal sensor combinations were found by considering the noise-estimation performance and the number of sensors installed. As a result, for the first case, the model showed an accuracy of 96.90% and a root-mean square error (RMSE) of 2.19, whereas the second case showed 97.40% and 2.31%, respectively. The noise information estimated by the model was visualized through simulation software for better field applicability. Detailed spatial and numerical noise information using the proposed system will help practitioners intuitively understand the real-time tendency of noise propagation, contributing to the determination of hot spots where reduction measures should be implemented. This information can also be used to more accurately identify workers continuously exposed to noise above dangerous levels and evaluate their actual cumulative noise exposure causing possible hearing damage.

Frequency compensation for in-ear noise dosimetry

Personal noise dosimeters (PNDs) are used in the prevention of noise induced hearing loss (NIHL) by monitoring noise exposure. Exposure of a worker is typically estimated by measuring the noise level at the shoulder location and subtracting the estimated protection provided by any worn hearing protection devices. Current noise dosimeters in the marketplace are deficient because the noise level at the shoulder does not necessarily represent the noise exposure in the ear canal due to shadowing, canal resonances and other acoustical phenomena. Moreover, workers often deliberately insert earplugs to shallow canal depths to better hear their environments, and earmuff attenuation is inconsistent due to variations in head geometry. Thus, the estimated hearing protection is often in error. In-ear personal noise dosimeters (iPNDs) can solve these problems because they incorporate a microphone within a hearing protection earplug that measures the actual noise level in an ear canal. However, the acoustics of the in-ear measurement are quite different compared to the shoulder measurement, and in-ear measurements must be frequency compensated to achieve valid results. This paper discusses in-ear noise dosimetry, why frequency compensation is needed and how to compensate the microphone signal. [This work was funded by a CDC SBIR grant.]

Give noise a voice so it can be better understood.

The purpose of the European Outdoor Noise Directive (OND) 2000/14/EC is to provide to the buyer of a machine relevant noise information so an informed buying decision favoring low noise machinery will be made. This in return should incentivize manufacturers over time to produce quieter machinery. However, the results of this strategy have yet to meet the expectations. Multiple issues have been recognized and investigated over the last years. As a result two guides were published, one for manufacturers to provide correct noise information and one for buyers in their purchases. Efforts are made to resolve issues with measurement standards. The actual noise label applied on OND-machines is also one of the identified issues at time of purchase. And for market surveillance it provides no real help. So what could be done to improve the situation? What key parts in the process involving different stakeholders and procedures should be addressed? This paper proposes a technical solution to provide understandable noise information to buyers in an effective way by leveraging mobile communication technology and by taking in account insights about human buying behavior. The solution could also help making market surveillance more effective.

Guidebook: how to reduce noise annoyance from road traffic

The objective of the FAMOS project was to develop a European guidebook with practical applications for the National Road Administrations on how to handle noise annoyance by non-acoustic moderators in planning of roads. When all technically feasible and economically possible measures to reduce the noise have been used, there can still be need for further reduction of the annoyance. Analyses reveal that only 1/3 of the variance in the annoyance response is caused by the noise level. 2/3 are determined by so-called non-acoustic factors. The annoyance response therefore can be altered without changes to the actual noise level. The project searched moderators that have a large impact on the annoyance without changing the noise level. The search was an international literature survey on annoyance studies. Moderators found were: •Expectations to and visual appearance of noise barriers •Presence of vegetation/greenery •Access to a quite façade •Neighbourhood soundscape •Attitudes towards authorities and road owners •Perceived traffic safety Experimental tests using sound walks, questionnaires and listening tests have been performed to quantify some moderators. Various methods have been used to find, extract and analyse data and turn the results into models for moderators formulated for practical use.

Hearing Care: Safe Listening Method and System for Personal Listening Devices.

Excessive use of Personal Listening Devices (PLDs) and prolonged exposure to noise from loud music create many potential risks associated with hearing loss. To this end, the World Health Organization has published Recommendation ITU-T H.870 in 2019, which provides adults and children with a set of recommendations for sound dosage and operating times needed to avoid potential hearing risks. Some studies have investigated noise exposure of related applications for listening safety, resulting in some related recommendations and applications; however, these studies often do not pay attention to measurement error, which is important for human real noise exposure estimation to avoid hearing loss. This paper proposes a method for calculating noise exposure that can accurately calculate the actual noise sound-pressure level (SPL) and PLD dosage based on the WHO-ITU standard. We develop a calculation method and design a listening system that includes (i) a Safe Listening Personal Listening Device (SL-PLD) that can measure the listening dose in real time and control the output volume effectively, (ii) a Safe Listening Application (SL-APP) for assisting the SL-PLD to check the listening status in real time and provide alerts. Our experimental results show that (i) the proposed noise calculation method can reach 0.88 dB deviation under the 76 dB reference SPL and 98.8% accuracy, as compared to the SoundCheck tool measurement, (ii) the proposed SL-PLD controls the SPL output effectively as the dose increases, and (iii) the SL-APP determines the dosage usage and will provide a warning when the dosage exceeds a preset value. Therefore, users can adjust their listening behavior for more secure listening by using our methods and applications.

Adaptive denoising combined model with SDAE for transient stability assessment

• A targeted denoising model (TDM) is constructed to obtain the satisfactory results when the data with similar degree of noise input. • A noise recognition model is constructed to output a set of weight values which can reflect the distribution of the noise in the input data. • An ADCM composed of multiple strong classifiers TDMs and the noise recognition model is proposed. ADCM can adaptively output the results with high confidence when the real data with different noise interference are input. Transient stability assessment (TSA) is usually considered as a challenging problem in power systems. During the measurement and transmission of real-time data in power grids, different degrees of noise may occur. The real noise is complex and hard to predict, so the current methods cannot output satisfied TSA results. Therefore, an adaptive denoising combined model (ADCM) is proposed in this paper. The ADCM can adaptively output the results of TSA in complex noise environment. Firstly, the stacked denoising auto-encoder based model trained by data with expected noise is proposed, which is called targeted denoising model (TDM). The TDM can output satisfied results for specific noisy data. Then, the ADCM composed of multiple TDMs and a noise recognition model is constructed. The recognition model is a regression model based on deep neural networks. When actual data are input, the recognition model can output weight values for TDMs. And when the actual noise is similar to the noise trained for the TDM, the weight obtained is great for this model. The final results are obtained by assigning the weights to the results of each TDM. The effectiveness of this method is verified by simulation results in IEEE 39-bus system and realistic system.

Moderation of people's subjective reactions to road noise

When the road administrations have used all technically feasible and economically possible measures to reduce the noise, there can still be a need for further reduction of the annoyance. Analyses reveal that only about 1/3 of the variance in the annoyance response is caused by the noise level itself, the other 2/3 are determined by so-called non-acoustic factors. The annoyance response therefore can be altered without changes to the actual noise level. The FAMOS project quantify how different factors modify people's subjective reactions to traffic noise. A literature survey has been carried out and experimental tests using sound walks, questionnaires and listening tests have been performed. The result was a European guidebook with practical applications for the National Road Administrations on how to handle noise annoyance by non-acoustic moderators in planning of roads etc.

Experimental Evaluation of Display Field Communication Based on Machine Learning and Modem Design

Display field communication (DFC) is a frequency-domain unobtrusive display-to-camera (D2C) communication, in which an electronic display serves as a transmitter and a camera serves as a receiver. In this paper, we propose a machine learning-based DFC scheme and evaluate its performance in a lab test scenario. First of all, we adopt the Discrete Cosine Transform (DCT) to transform a spatial-domain image into its spectral-domain equivalent. To reduce the computational complexity during the data-embedding process, addition allocation and subtraction data retrieval techniques are used. Moreover, channel coding is applied to overcome the data error caused by the optical wireless channel. In particular, robust turbo coding is used for error detection and correction. Afterward, we perform the experiments to validate the performance of the proposed system. After capturing the displayed image with a camera, data restoration is done using a deep learning technique. Extensive real-world experiments were performed considering various geometric distortions, noise, and different standard input images. As a result, we found that by increasing the transmit display image size (upsampling), the overall error rate can be reduced. In addition, real-world noise analysis is performed and it is notified that the actual noise is dominant in the low-frequency region of an image. The experimental results confirm the robust performance of the proposed DFC scheme and show that an error-free performance can be achieved up to a distance of 1m in the given lab test environment setting.

Active control of vehicle interior engine noise using a multi-channel delayed adaptive notch algorithm based on FxLMS structure

A multi-channel delayed adaptive notch algorithm based on the filtered-x least mean square (FxLMS) structure was proposed for active control of vehicle interior engine noise with prominent harmonic characteristics. First, a complete reference signal model was established utilizing vehicle engine speed. Second, the algorithm procedure was presented systematically, and a new time-delay method was introduced to simplify the filtering process of the reference signal to greatly improve the calculation efficiency. Furthermore, the convergence condition was derived, and mathematical models of the steady-state error and convergence speed were obtained. The simulation results, which were consistent with theoretical calculation values, showed that the two models were highly accurate. They help to theoretically indicate the interval range of the effective convergence coefficient μ and its optimal value. Using the proposed multi-channel delayed adaptive notch algorithm, actual vehicle active noise control experiments were conducted. The results show that, under stationary conditions, the interior overall engine noise reduction in the time-domain reaches 4.05 dB (A), and the corresponding second-order engine noise reduction reaches 13.66 dB (A), however, under nonstationary working conditions, the proposed algorithm still has a good noise reduction effect. This algorithm can be directly applied to the active control of vehicle engine noise and other similar narrowband noise reduction.