Background Noise Measurements Research Articles

Automatic estimation of the wind turbine noise with recurrent neural networks

There is growing interest in the development of renewable energies, particularly wind power. However, wind turbines generate noise that can affect the sound environment of nearby residents. This study focuses on the isolation of wind farm noise (WFN) from the surrounding ambient noise. Our method is based on a Recurrent Neural Network (RNN) Architecture that captures temporal dependencies in the acoustic signal. This proposal is compared to Non-Negative Matrix Factorization (NMF) that has shown first promising results on a previous study on simulated sound scenes. Our approach relies on Long Short Term Memory (LSTM) RNN, conducted using an end-to-end trained model and a Gated Recurrent Unit (GRU). The training and testing dataset is constructed by superimposing measured background noise with synthesized wind turbine noise, effectively simulating realistic environmental conditions. This study aims to reduce the acoustic impact of wind turbines on communities and attempt to control turbine operation using advanced machine learning techniques.

Detection of Methane Leaks via a Drone-Based System for Sustainable Landfills and Oil and Gas Facilities: Effect of Different Variables on the Background-Noise Measurement

In recent years, thanks to the great diffusion of drone technology and the development of miniaturized sensors that can be connected to drones, in order to increase the sustainability of landfills and oil and gas facilities, interest in finding methane leaks and quantifying the relative flow has grown significantly. This operation requires the methane background concentration to be subtracted from the calculations. Therefore, in order to proceed with a right estimate of CH4 flows emitted, the possibility of correctly measuring or estimating the background level becomes crucial. The present work intends to illustrate the effects of different variables on the background-noise measurement in a drone-based system that uses a tunable diode laser absorption spectrometer (TDLAS). The methodology used is that of field testing; the data acquisition campaign consisted of the execution of 80 flights during which different flight variables (drone speed, flight altitude) were tested; the flights were repeated in different weather and climate conditions both during the same day and in different periods of the year. Different surfaces, similar to those found in landfill or natural gas sites, were also tested. In some of the field trials, a controlled methane release test was performed in order to verify how much the quantification of the methane flow can vary depending on the background level used. The results of the different field trials highlighted the best conditions under which to measure methane emissions with a TDLAS sensor in order to minimize the number of outliers: flight altitude not exceeding 15 m above ground level; the drone speed appears to have less impact on the results, however, it is optimal between 1 and 2 ms−1; a very sunny day produces much higher methane background levels than a cloudy one. The type of surface also significantly affects the measurement of background noise. Finally, tests conducted with a controlled methane release highlighted that different levels of background have a significant impact on the estimation of the methane flux emitted.

Analysis of the frequency-time structure of acoustic noises of unmanned aerial vehicles in the STM32 CubeIDE environment

The formulation of the task of detecting small unmanned aerial vehicles (drones) is presented, the expediency of building a drone detection system in the stm32 cubeide environment based on the principle of reception and analysis of acoustic signals emitted by drones during their flight mission is substantiated.
 The study of temporal fluctuations in the period of acoustic signals of a drone is carried out by the method of model-correlation analysis, as a result of which three-dimensional structures are formed: time – period – correlation coefficient of the acoustic signal with the model in the form of a time-limited sinusoidal function.
 The resulting structures are formed as matrices of correlation coefficient values.
 The members located along the columns are calculated by time shifting the model function along the signal sample. The members in each column are calculated with a constant period of the model function given from a series of values.
 It is shown that the correlation coefficients between the rows of the matrices calculated from drone signals are significantly higher than the same values obtained from background noise measurements. The functions showing the change in time of the correlation coefficients between the rows of the time-period matrix structures for drone signals and background noise do not overlap and show a consistently larger difference in correlation coefficients, which allows us to use the correlation coefficient as a feature that classifies the presence of drone signals.

Adaptive milling chatter identification based on sparse dictionary considering noise estimation and critical bandwidth analysis

As one of the most unfavorable factors, chatter will seriously hinder the improvement of production efficiency. The weak chatter identification is an effective method for stable cutting. In this paper, a weak chatter identification method is developed using sparse dictionary on account of the milling dynamic responses. As the bases for chatter identification, chatter frequencies are calculated based on the milling dynamic equations. In order to decrease false alarm rate due to the measured background noise, the noise estimation based filtering is proposed. Besides, in consideration of the parameter uncertainty, an adaptive critical bandwidth analysis is implemented with the developed energy proportion index in case of missed alarm. Then, the sparse dictionary matrix is constructed based on the obtained chatter frequencies and critical bandwidth. Finally, the gradient projection for sparse reconstruction (GPSR) algorithm is adopted for chatter frequencies extraction. The proposed method can decrease the measured background noise, obtain critical chatter band and extract weak chatter frequencies accurately. Experimental results shows that the developed method can be successful in weak chatter identification.

Masking effect of HVAC noise on walking sounds on concrete floors

Background noise masks living sounds in apartment buildings. Depending on the sound insulation properties of the structures of the apartments, background noise affects the audibility of the perceived airborne and impact sounds from neighboring dwellings. This preliminary study focused on the masking effect of the background noise generated by HVAC systems on the impact sounds generated by walking on concrete floors. The study was carried out by determining the signal-to-noise ratios of walk induced sound levels on the concrete floors and measured background noise levels. The walking sounds were generated by walking with socks and shoes on nine concrete floors, and the sound pressure levels were measured in a receiving room below the floors. The background noise levels, where these walking sounds were compared, consisted of the results collected in 210 measurements carried out in Finnish apartment buildings. The results of the study show how it is likely that the background noise masks sound from walking with socks and shoes differently. To investigate the phenomenon more in detail, follow-up research has been proposed.

Adaptive Neuro-Fuzzy-based Vibration Approach for Structural Fault Diagnosis

Acoustic comfort in classroom is significant to protect the health and enhance scholastics performance of both students and teachers. The aim of this study is to evaluate the acoustic comfort in a primary school of Sekolah Kebangsaan Bukit Soga, Batu Pahat through objective and subjective evaluations. Field measurements were carried out to measure the background noise and reverberation time inside the classrooms. Six classrooms were tested to investigate the acoustic performance of the learning spaces of this school. Besides, questionnaires survey and interviews have also been conducted to assess the acoustic comfort of the teachers on the teaching and learning process. The measured background noise and reverberation times inside all the selected classrooms were very high comparing to the recommended values. The results from the survey showed that teachers were not satisfied with the acoustics comfort of this school’s acoustic environment.

Distribution Capacitor Health Monitoring Based on the Disturbances of Element Breakdown Faults

As a common incipient fault for a compensation capacitor, the internal capacitor element breakdown fault (CEBF) causes slight changes in the steady-state voltage and current, which are difficult to accurately capture under the influence of measurement error. Therefore, to meet the demand for intelligent equipment maintenance by a smart grid, transient disturbances induced by a CEBF are used in this study to acquire element breakdown information for online monitoring of capacitor health. A transient response analysis of the CEBF in the time and frequency domains is used to establish the typical transient characteristics. Then, a multiple-kernel support vector machine algorithm is adopted to identify the transient disturbances. A method for determining the faulty capacitor unit is proposed based on the position of the waveform monitor. Under the assumption and statistics of the number of failed elements in a series group, a scheme for assessing the health condition is investigated. The proposed method can effectively obtain breakdown information to assess capacitor health with little influence of measurement error and background noise. The results of a theoretical analysis, computer simulation, lab experiment and field monitoring verify the effectiveness of the proposed method.

Highly selective and multicolor ultrasensitive assay of dipicolinic acid: The integration of terbium(III) and gold nanocluster

A novel multicolor fluorescent nano-probe based on the hybridization of Tb3+ ion with gold nanoclusters (Au NCs) was synthesized to monitor and on-site visual assay of 2,6-pyridinedicarboxylic acid (DPA), a biomarker of bacterial spores. DPA can replace the water molecule in the center of Tb3+ and strongly coordinate with Tb3+ based on the analyte-triggered antenna effect. Simultaneously, the red fluorescence of Au NCs is not influenced after addition of DPA and can be used as steady inside fluorescence reference channel to measure background noise. On this basis, the multicolor fluorescence nano-probe based on Tb3+-doped Au NCs for fast analysis of DPA was fabricated. The linear range of this method is 0 to 12.5 μM and the limit of detection is 3.4 nM, which is well below the quantity of DPA concentration of 60 μM released by the spore transmission dose of anthrax infection. The proposed multicolor fluorescence nano-probe was successfully detecting DPA in actual sample with good sensitivity and specificity. In addition, the visual paper-based nano-probe is designed to detect DPA by using the color scanning application of smart phone. This developed platform possesses abroad application prospects with advantages of effective, convenient carrying, simple operation, good selectivity and repeatability.

Visualization of Acoustic Comfort in an Open-Plan, High-Performance Glass Building

The aesthetic and functional appeal of high-performance, open-plan office buildings presents special challenges. Extensive use of glass at the building’s perimeter to improve visual comfort and office communication can negatively impact acoustic comfort without proper design considerations. This study investigates the utility of a novel visualization approach to documenting the interactional impact of acoustical comfort on the health and well-being of occupants in an open-office environment. Room acoustic measurements of background noise and speech transmission index were conducted and distraction distances were calculated and visualized using a mapping technique. In addition, a comprehensive pre- and post-occupancy evaluation protocol was employed. The paper illustrates the reliability of the visualization approach to aid in the interpretation and comparison of various open-office acoustic solutions from a human-centric acoustic environment perspective.

Optimization of System Design and Calibration Algorithm for SPAD-Based LiDAR Imager

This paper presents the optimization of system design and calibration algorithm for LiDAR imager based on the single-photon avalanche diode (SPAD) image sensor. The supply voltage and the gating high-voltage driving circuit of the SPAD is optimized based on theoretical analysis and experimental data to improve the trigger times of the SPAD for the laser echo. The real-time calibration algorithm is realized in field programmable gate array (FPGA) to obtain the flight time and improve measurement reliability. A 10-bit time-efficient and area-efficient shifted inter-frame multi-event histogram and bubbling-based peak detection algorithm are used to calculate flight time, which requires the memory as low as 16 times smaller than storing complete histogram if the pixel values are coded on up to 10 bits and can detect up to four different targets simultaneously without increasing the measurement time. The performance of different kinds of background noise measurement methods is analysis based on experimental data, which shows that the measurement results are more accurate with the increasing of TDC event detection ability. The proposed crosstalk suppression algorithm based on LiDAR transmission function can reduce optical crosstalk between adjacent pixels to improve the measurement reliability of the LiDAR imager. The LiDAR imager is presented capable of measuring 10 m range with 70 Klux background noise and 30°×45° field of view.

Evaluation of Frequencies for the IoT Telemetry in Smart Agriculture

The IoT is becoming a widely known technology for the gathering of telemetry data, while mostly the concept of Smart cities is usually seen as the most challenging area for implementation. The different situations can be found in the smart agriculture concept, where different requirements and especially conditions exist. The purpose of this paper is to make an overview of IoT frequency bands available, with special focus on the situation in the EU, their theoretical usability and, using experimental measurements of typical background noise in different bands and calculations of transmission reliability on expected distance, estimate the practical usability of those technologies in the smart agriculture, compared to the smart city’s requirements. Most of the IoT installations outside 5G systems are in the 900 MHz band, but is this well-suitable for smart agriculture?

Improvements of a Hard-Wall Closed Test-Section of a Subsonic Wind Tunnel for Aeroacoustic Testing

For achieving accurate aeroacoustic measurements to the aircraft industry, a low-speed wind tunnel, primarily designed for aerodynamic testing, is modified to provide lower background noise environment. Based on data from single microphone at different wind tunnel locations and microphone phased-array measurements inside the test-section, the main noise sources are identified and feasible alternatives are implemented for reducing the background noise such as new acoustically treated corner-vanes and sidewall lining located upstream the drive system. The acoustically transparent concept for the test-section is also investigated showing promising results for further improvements in the wind tunnel. Results are presented for sound pressure levels from single microphone measurements at different locations in the wind tunnel as well as from the beamforming array inside the test-section. Background noise measurements before and after improvements confirm that the ability of performing aeroacoustic tests has significantly increased with noise reduction of 5 dB inside the test-section.

A Bayesian approach to eliminate correlated noise using an independent reference-Application to supersonic jet noise extraction.

A Bayesian method to remove correlated noise from multi-channel measurements is introduced. It is based on Bayesian factor analysis coupled with prior but uncertain knowledge of the correlation structure of the noise. This technique is well suited to denoise cross-spectral matrices measured in the frame of aeroacoustic experiments when background noise measurements are available, because it allows separating the engine noise contribution from the turbulent boundary layer and uniform noise components that are all sensed by in-flow microphones. In-flight data measured on flush-mounted microphones on an aircraft fuselage are denoised using this method. It is shown that it has a significant benefit for studying the broadband shock-associated noise generated by the engines in realistic flight conditions.

Acoustic characterization of Zea Mays culm fibers (corn) and Musa X Paradisiacal (banana) stem fibers in proportion 50% - 50%.

The following research arises as a proposal to the implementation of Zea Mays culm fibers and Musa X Paradisiaca stem fibers in proportion 50% - 50% for the development of a new eco-material with acoustic properties, the objective was to measure the absorption coefficient based on the international standard ISO 10534-1 Determination of the acoustic absorption coefficient and acoustic impedance in impedance tubes. Using the impedance tube to identify the minimums and maximums needed to perform the computational procedure in the MATLAB software tool, and finally obtain the sound absorption coefficients of the material. The measurement process is supported by the implementation of the impedance tube, having all its consideration and previous measures that support the veracity of the data taken through this process, in addition to the fact that background noise measurements were made in order to pass these values to ensure reliable results in the measurement, performed with a class 1 sound level meter; The fibers analyzed had a range between 0.8136 and 0.9225 absorption coefficient in the bands of 1000, 2000 and 4000 Hz, testing the effectiveness in their implementation as an acoustic barrier. Keywords: absorption, eco-material, fibers, barrier, acoustics.

Simulations of vehicular optical wireless communication systems and comparisons with DSRC systems.

Optical wireless communication (OWC) has been proposed as a complementary technique to radio frequency (RF) communications for vehicular applications. OWC systems can be categorized into two types based on the transmitters: the first one is the light-emitting diode (LED)-based OWC system, and the second is the laser diode (LD)-based OWC system. Simulations of both types of OWC systems are presented in this paper. To simulate the OWC systems precisely, outdoor experiments of OWC systems have been done and the measurements of background noise are applied in the simulations. In terms of the LED-based OWC system, the impulse responses are obtained by an improved ray tracing algorithm. To reduce the computational complexity, visibility graphs are applied in the improved ray tracing algorithm. Compared with the brute force algorithm, our improved algorithm is able to reduce the computational complexity from O(n3) to O(n2log⁡(n)), where n is the number of mobile terminals. In LD-based OWC systems, the performance and stability are highly dependent on the tracking system in vehicular applications. Therefore, this paper also analyzes the requirements of tracking accuracy in LD-based OWC systems. Finally, the simulated LED-based OWC system is compared with the dedicated short-range communication (DSRC) system under different traffic densities. Our experimental and simulation results have demonstrated that OWC can be a complementary technique for DSRC under conditions of high traffic density.

Measurement of Noise Emission Level at Substation Boundary Based on Coherent Power

According to the Chinese standard of GB 12348-2008, to determine the noise emission level at substation boundary, the noise level of substation boundary and the background should be measured separately. Then correct noise level at substation boundary utilizing measured background noise. However, the measured background noise is inaccurate due to the different acoustic environment. Therefore, the noise emission level at substation boundary applying this method often has a certain error. This paper proposes a method for separating transformer noise to calculate the noise emission level of transformers based on coherent power, which avoids the error caused by the inaccurate measurement of background noise.

Gold nanocluster-europium(III) ratiometric fluorescence assay for dipicolinic acid.

Aratiometric fluorescence assay was designed for determination of dipicolinic acid (DPA), a spore-specific compound which is used as a biomarker for Bacillus anthracis spores for food and medical product safety analysis. The dual-channel fluorescence probe integrates two fluorescent materials, Eu3+ ion and gold nanocluster (Au NC). The Au NC is used as a reference channel to measure background noise and the Eu3+ ion as the DPA-specific response signal channel. The probe was prepared through simply combing bovine serum albumin (BSA)-scaffolded Eu3+ ion and Au NCs. When excited at 530nm, in the presence of DPA, the fluorescence signals of Eu3+ ion at 595, 617, and 695nm increased significantly while the 650nm signal of Au NC reference remained relatively constant. This fluorescence probe has good photo-stability and also displays good selectivity and high sensitivity for DPA with a low detection limit of 0.8μM. The linear range of the ratiometric probe for DPA is 1-50μM. For determination of DPA released during the germination of Bacillus subtilis spores, the detection results were in agreement with measurements by conventional calorimetry assay. The method may have potential for measuring the level of contamination and germination by spores. Graphical Abstract Dual-channel fluorescence biosensor was designed to detect dipicolinic acid, a spore-specific compound which is used as a biomarker for Bacillus anthracis spores for food and medical product safety analysis.

Penilaian Kualitas Akustik Masjid Raudhaturrahman Padang Tiji Menggunakan Simulasi Ecotect

Mosque is a place of worship for Moslems. It has profound religious significance for the community; it is important that people feel serenity therein. In addition, they need to be able to hear sounds clearly so that they can perform their religious rituals like prayers smoothly and it is also crucial for them to hear speeches with a high degree of intelligibility. This study assessed the acoustic quality of Raudhaturrahman prayer's room, having a unique shape with the dome ceiling covering all dimensions of the building. This study aims at finding the condition, causes, and improving the acoustic quality to meet the prayer's room acoustic needs. This study specifically focused on calculating the background noise and reverberation time using the Ecotect Simulation method. The simulation result demonstrates that the calculation of reverberation time (RT) and the measurement of background noise have not met the desired standard. This study found that the value for RT with occupancy 0%, 50%, 100% is 18, 29s, 7, the 80s, and 4, 71s. In addition, the result of the background noise is 60 dB. In conclusion, the acoustic of the Raudhaturrahmah mosque prayer's room is of inferior quality since it does not meet the standard of reverberation time (RT) and background noise for the speech purpose of the room.

Magnetic Dipole Target Signal Detection via Convolutional Neural Network

In this letter, two convolutional neural network (CNN) models for detection of magnetic anomaly target signals are proposed. One is a 1-D CNN combined with signal feature (SF-CNN1D), and the other is a 2-D CNN based on time–frequency diagrams (TF-CNN2D). To train the models, simulated signals are added to the measured background noise to generate the positive sample set, while the negative sample set is the pure measured noise. Simulation results show that both of the models have satisfying train and test accuracies. A field experiment is conducted to verify the generalization ability of the two CNN models to real data. It is demonstrated that the two CNN models have good detection performances and are some better than the conventional support vector machine (SVM) approach by several percent in colored Gaussian noise scenarios.

Аnalysis of frequency-time structure of acoustic noise of small automatic air systems

The statement of the problem of detecting small automatic air systems (drones) is formulated, the expediency of building a drone detection system on the principle of receiving and analyzing acoustic signals emitted by drones during their flight mission is substantiated.The study of temporal fluctuations of the period of the acoustic signals of the drone is carried out by the method of model-correlation analysis, as a result of which three-dimensional structures are formed: time – period – the correlation coefficient of the acoustic signal with the model in the form of a sinusoidal function limited in time.The resulting structures are formed as matrices of correlation coefficient values.The members located along the columns are calculated with the time shift of the model function along the signal sample. The members in each column are calculated for a constant period of the model function set from a number of values.It is shown that the correlation coefficients between the matrix rows calculated from the drone signals are significantly higher than the same values obtained from the background noise measurements. The functions showing the change in time of the correlation coefficients between the rows of the matrices of the time – period structures for drone signals and background noise do not intersect and show a consistently large difference in the correlation coefficients, which allows the correlation coefficient to be used as a classifying feature when recognizing drone signals.