Real-time Noise Research Articles

A Study of Urban Noise Pollution in Jalalabad City of Nangarhar Province, Afghanistan

This study examines the pervasive issue of global noise pollution and its implications for human health, specifically concentrating on 16 high-traffic locations within Jalalabad, Afghanistan, utilizing the Sound Meter application for data acquisition. The conspicuous absence of antecedent research accentuates the significance of this inquiry. Analysis identified traffic, loudspeaker systems, and human activities as primary contributors to the ambient noise. Over a meticulously documented 32-day period in March and April of 2023, notable noise peaks were observed, notably at Talshay Square, registering 73.5 dB at 3:00 PM, and at Malang Jan Watt, reaching 87.5 dB at noon. Urgent intervention is imperative to ameliorate these conditions for the well-being of the city's residents, urging policymakers to implement efficacious noise abatement strategies. This research offers pivotal real-time noise data for Jalalabad and suggests mitigation measures, stressing the necessity for further scrutiny to assess strategy effectiveness, aiming for a harmonious urban environment.

High-speed in vivo calcium recording using structured illumination with self-supervised denoising

High-speed widefield fluorescence imaging of neural activity in vivo is fundamentally limited by fluctuations in recorded signal due to background contamination and stochastic noise. In this study, we show background and shot noise-reduced imaging of the ultrafast genetically encoded Ca2+ indicator GCaMP8f in CA1 pyramidal neurons using periodic structured illumination (SI) with computational image reconstruction. We implement what we believe to be a novel reconstruction method for data acquired using periodic structured illumination, termed pseudo-HiLo (pHiLo), that combines a pseudo-widefield (pWF) reconstruction with individual SI frames to perform a HiLo reconstruction. We compare this new technique to interleaved optical sectioning structured illumination microscopy (OS-SIM) and pWF reconstruction. We quantify the performance of each reconstruction by evaluating contrast, transient peak-to-noise ratio (PNR), pairwise correlation coefficients between ΔF/F time courses extracted from individual in-focus cells, and correlation coefficients between each cell with surrounding cell-free background pixels. We additionally incorporate a self-supervised deep learning method for real-time noise suppression (DeepCAD-RT) into our data preprocessing pipeline. At 500 Hz frame rates, we demonstrate a 75% increase in PNR using the denoised pHiLo reconstruction compared to pWF. Utilizing DeepCAD-RT, we show significant PNR improvements using both structured illumination (SI) reconstruction methods with OS-SIM showing a 59% increase in PNR after denoising. Both pHiLo and OS-SIM reconstructions result in a ≈65% decrease in the mean correlation coefficient of the ΔF/F time courses between ROIs in comparison with pWF, indicating the potential to remove background fluorescent transients from out-of-focus cells.

Individual-level noise exposure and its association with sleep quality and duration: A cross-sectional study using real-time data

As urban populations grow, the problem of noise pollution becomes more significant. The limited number of epidemiological studies linking individual-level dynamic noise exposure to sleep highlights a gap in our understanding of how individual-level noise exposure impact sleep quality and duration. A cross-sectional survey was conducted in Hong Kong, and portable noise sensors were used to record participants' real-time noise exposure. The Pittsburgh Sleep Quality Index was used to assess their sleep quality. Logistic regression was used to examine the relationship of individual-level noise exposure with sleep quality and sleep duration. Among a total of 763 subjects included in the analysis, and a-weighted decibels [dB(A)] 24-h average exposure (L24) was 63.21 (58.85–67.85) dB(A) for workday and 63.52 (59.03–67.86) dB(A) for non-workday. A significant decrease in individual noise level was observed closer to bedtime (P < 0.05). Compared with the lowest noise exposure, exposure to the highest noise level was positively associated with the presence of poor sleep quality for L24, Ld and Ln, with an odds ratio (OR) and 95 % confidential interval (CI) of 1.53 (1.04–2.24), 1.65 (1.12–2.43), and 1.51 (1.03–2.21), respectively. Exposure to elevated noise level was associated with increased risk of shorter nocturnal sleep duration (< 7 h), the OR and 95 % CI was 1.87 (1.29–2.73) for L24, 1.58 (1.09–2.30) for Ld, 1.50 (1.03–2.18) for Le, and 1.60 (1.10–2.32) for Ln in the highest noise exposure group. Similar findings were observed both on workdays and non-workdays, respectively. Furthermore, the results showed that each 10 dB(A) increment in L24 and Ln was associated with a probable 29 % and 27 % increase in poor sleep quality and a 24 % and 24 % reduction in sleep duration. This study demonstrates that environmental noise exposure was associated with poor sleep quality and shorter sleep duration, posing a significant public health issue in Hong Kong.

Online secondary path modeling algorithm without auxiliary noise for narrowband active noise control

Online secondary path modeling (SPM) is a practical method for real-time noise reduction in narrowband active noise control (NANC) systems, particularly when addressing variations in the secondary path. However, the common practice of using auxiliary noise for online SPM increases the residual noise power and deteriorates the noise reduction performance. The present study proposes a strategy that does not rely on auxiliary noise for online SPM in NANC systems. The proposed algorithm comprises two stages: Stage A models the primary path, whereas Stage B concurrently engages in online SPM and active noise control. The control signal is used to model the discrete Fourier transform (DFT) coefficients of the secondary path, avoiding the need for an auxiliary noise and significantly reducing the computational complexity. Moreover, the predicted primary path from Stage A is employed to obtain the pure desired signal of the online SPM. This strategy decorrelates the primary noise and the modeling signal, and accelerates the convergence of the algorithm. Simulations of recorded data demonstrate that the proposed algorithm can quickly track variations in both the primary and secondary paths, and maintain the noise reduction performance and stability of the system.

Developing a virtual physical system for vortex-induced vibration studies of a bluff body

A virtual physical system (VPS) for VIV studies of a bluff body is developed to replace the actual physical systems. It can arbitrarily and accurately control and edit the physical parameters, including mass, damping ratio and spring stiffness, specifically for the mass-spring-damper system. The recursive Duhamel integral method (DIM) with unconditional stability was used for the VPS control system, addressing real-time noise filtering problem and simplifying the system as a single input and single output (SISO) one. Delay compensation and inertial force elimination methods were investigated and proposed to overcome the crucial unwanted damping effects. An experimental facility for VIV model tests by VPS was manufactured, and the bluff body model with a measurement system was specially designed to accurately sense the hydrodynamic force during VPS operation. Systematic verification experiments for parameter editing and control of an actual physical target system were conducted, showing that the VPS can reproduce the equivalent spring-damper-mass system in high fidelity with an accuracy error of less than 5 %. VIV model tests for a bluff body at Reynolds numbers (Re= UD/υ, where U is the flow velocity, D represents the diameter of cylinder model, and υ is the kinematic viscosity coefficient) of 5.7E4 and 2.3E5 were performed using the VPS experimental facility, presenting well-Repeated VIV responses at low Re and unexpected VIV response with a large amplitude of 2.4 D at high Re, which can cause severe fatigue damage for relevant structures. The present VPS will provide promising and powerful experimental tools for VIV studies of a bluff body to reveal the related sensitive parameter effects.

Non-linear associations between noise level and people's short-term noise annoyance in different activity contexts

Recent research has become increasingly interested in the on-linear associations between noise levels and people's short-term noise annoyance. However, there has been limited investigation into measuring short-term noise annoyance and how different activity contexts may affect these non-linear associations. To address this research gap, this study measured people's short-term noise annoyance using real-time Ecological Momentary Assessment (EMA) data and the Day Reconstruction Method's (DRM) recalled data. Corresponding noise levels were captured using Global Positioning Systems and portable noise sensors. Employing the Shapley additive explanations method, we examined the non-linear associations between noise level and people's real-time and recalled noise annoyance across different activity contexts. The results indicated that 1) People had greater sensitivity to noise levels in real-time annoyance (non-linear association threshold: 60 dB) compared to recalled annoyance, which had a higher non-linear association threshold of 70 dB. 2) The non-linear associations between noise level and people's real-time/recalled noise annoyance varied between different activity contexts. People tended to be more sensitive to noise in real-time annoyance than recalled annoyance on travel routes and at workplaces. 3) Among the factors examined, the contribution of noise level varied across activity contexts. Noise level contributed more significantly to people's real-time noise annoyance in outdoor recreational sites and on travel routes. These findings enhance our understanding of the non-linear association between noise level and people's short-term noise annoyance, moving beyond the linear paradigm. Policymakers should consider the non-linear relationships and different activity contexts when implementing noise control measures.

Devising measures to reduce multi-frequency noise load on employees in machining area

The object of this study is the process of determining and managing the risks of noise exposure (NE) for employees at the mechanical department when machining metals on a drilling machine (DM). The problem relates to the increase in the risk of an employee receiving an industrial injury because of NE at the workplace of a machine tool. The impact of noise on the human body depends on the duration of exposure, the level of sound pressure, and its intensity. An important characteristic of noise is its frequency composition, as it can affect the perception of sounds and the human body. Prolonged exposure to noise can damage various systems in the human body or cause pain. Therefore, it is important to understand the impact of NE on workers and devise measures to reduce the negative impact on their health and work productivity. The research is based on real-time noise measurements followed by their decomposition into octave frequencies and modeling of noise propagation in the room with and without the use of various types and designs of soundproof barriers (SB). In the course of the study of NE propagation during drilling on the machine, an excess of noise levels at medium and high frequencies near the noise source was found. An employee who works on DM is exposed to high-frequency noise that exceeds the established normative indicators. Therefore, it is necessary to use personal protective equipment for the machine operator and install safety equipment to protect other workplaces. The use of the latter makes it possible to significantly reduce the impact of NE on workers, in particular, at low frequencies by 20.8 %, at medium frequencies by 15.6 %, and at high frequencies by 17.3 %

Intelligent Substation Noise Monitoring System: Design, Implementation and Evaluation

In recent years, the State Grid of China has placed significant emphasis on the monitoring of noise in substations, driven by growing environmental concerns. This paper presents a substation noise monitoring system designed based on an end-network-cloud architecture, aiming to acquire and analyze substation noise, and report anomalous noise levels that exceed national standards for substation operation and maintenance. To collect real-time noise data at substations, a self-developed noise acquisition device is developed, enabling precise analysis of acoustic characteristics. Moreover, to subtract the interfering environmental background noise (bird/insect chirping, human voice, etc.) and determine if noise exceedances are originating from substation equipment, an intelligent noise separation algorithm is proposed by leveraging the convolutional time-domain audio separation network (Conv-TasNet), dual-path recurrent neural network (DPRNN), and dual-path transformer network (DPTNet), respectively, and evaluated under various scenarios. Experimental results show that (1) deep-learning-based separation algorithms outperform the traditional spectral subtraction method, where the signal-to-distortion ratio improvement (SDRi) and the scale-invariant signal-to-noise ratio improvement (SI-SNRi) of Conv-TasNet, DPRNN, DPTNet and the traditional spectral subtraction are 12.6 and 11.8, 13.6 and 12.4, 14.2 and 12.9, and 4.6 and 4.1, respectively; (2) DPTNet and DPRNN exhibit superior performance in environment noise separation and substation equipment noise separation, respectively; and (3) 91% of post-separation data maintains sound pressure level deviations within 1 dB, showcasing the effectiveness of the proposed algorithm in separating interfering noises while preserving the accuracy of substation noise sound pressure levels.

Design of a Website-Based Realtime Noise Monitoring System in the Work Environment as a Support for Occupational Health and Safety (OHS) Data

The level of noise in the work environment is one of the factors that affect workers' performance. Research on noise level monitoring tools has been realized using a GY Max 4466 sound sensor with a website-based ESP Wroom 32 microcontroller. This study aims to determine the noise level in the work environment and to monitor noise levels based on a website to support occupational health and safety data. Data was collected by sound detection in the PDAM Way Rilau Bandar Lampung pump room from 08.00-12.00 WIB. The results showed that the system functioned well, indicated by the average accuracy of the four sensors of 97.07% and the average error value of 2.93%. The noise level data generated from the PDAM Way Rilau pump room is classified as high or dangerous because it has exceeded the noise threshold (NAV) value of 70 dB. The system used on the website is localhost access on a computer that can be monitored in the work environment as supporting data for occupational health and safety (K3).

Image Domain Multi-Material Decomposition Noise Suppression Through Basis Transformation and Selective Filtering.

Spectral CT can provide material characterization ability to offer more precise material information for diagnosis purposes. However, the material decomposition process generally leads to amplification of noise which significantly limits the utility of the material basis images. To mitigate such problem, an image domain noise suppression method was proposed in this work. The method performs basis transformation of the material basis images based on a singular value decomposition. The noise variances of the original spectral CT images were incorporated in the matrix to be decomposed to ensure that the transformed basis images are statistically uncorrelated. Due to the difference in noise amplitudes in the transformed basis images, a selective filtering method was proposed with the low-noise transformed basis image as guidance. The method was evaluated using both numerical simulation and real clinical dual-energy CT data. Results demonstrated that compared with existing methods, the proposed method performs better in preserving the spatial resolution and the soft tissue contrast while suppressing the image noise. The proposed method is also computationally efficient and can realize real-time noise suppression for clinical spectral CT images.

Linearized Models of the Coupled Rotorcraft Flight Dynamics and Acoustics for Real-Time Noise Prediction

This article demonstrates the linearization of the coupled rotorcraft flight dynamics and aeroacoustics to provide realtime acoustic predictions in generalized maneuvering flight. To demonstrate the methodology, the study makes use of a nonlinear simulation model of a generic utility helicopter (PSUHeloSim) that is coupled with an aeroacoustic solver based on a marching cubes algorithm. A periodic equilibrium of the coupled rotorcraft flight dynamics and acoustics is first found at a desired flight condition using a modified harmonic balance trim solution method. Next, the nonlinear time-periodic dynamics are linearized about that periodic equilibrium and transformed into an equivalent higher order linear time-invariant system in harmonic decomposition form. Composite aeroacoustic measures are included as an output of this system. To speed up runtime and make control design tractable, the order of these harmonic decomposition models is reduced via residualization to an 8-state model where the states are representative of the rigid-body dynamics of the aircraft. This 8-state model is shown to provide accurate acoustic response predictions for small-amplitude pilot inputs and to abate runtime by a factor of approximately 104, thus enabling acoustic predictions in generalized maneuvering flight that are significantly faster than real time. The 8-state model is subsequently used to demonstrate the use of linear system tools for the dynamic analysis of the coupled rotorcraft flight dynamics and acoustics.

Is it possible to develop a digital twin for noise monitoring in manufacturing?

Noise monitoring is important in the context of manufacturing because it can help maintain a safe and healthy workspace for employees. Current approaches for noise monitoring in manufacturing are based on acoustic sensors, whose measured sound pressure levels (SPL) are shown as bar/curve charts and acoustic heat maps. In such a way, the noise emission and propagation process is not fully addressed. This paper proposes a digital twin (DT) for noise monitoring in manufacturing using augmented reality (AR) and the phonon tracing method (PTM). In the proposed PTM/AR-based DT, the noise is represented by 3D particles (called phonons) emitting and traversing in a spatial domain. Using a mobile AR device (HoloLens 2), users are able to visualize and interact with the noise emitted by machine tools. To validate the feasibility of the proposed PTM/AR-based DT, two use cases are carried out. The first use case is an offline test, where the noise data from a machine tool are first acquired and used for the implementation of PTM/AR-based DT with different parameter sets. The result of the first use case is the understanding between the AR performance of HoloLens 2 (frame rate) and the setting of the initial number of phonons and sampling frequency. The second use case is an online test to demonstrate the in-situ noise monitoring capability of the proposed PTM/AR-based DT. The result shows that our PTM/AR-based DT is a powerful tool for visualizing and assessing the real-time noise in manufacturing systems.

Improvements to a mobile app for community noise and assessment data collection

A mobile phone application was developed to collection health history, real time noise levels and participants' perceptions. The goal of this application is to collect data that can directly link community response, noise levels and community health from a large number of participants. This will help identify where noise is causing issues related to physical and mental health on a community scale. In order to identify these effects at a community level large numbers of responses are critical. Ease of use is essential to ensure that participants remain willing to participate in the study and continue to submit samples. This needs to be balanced with the usefulness of the data collected by the public. Previously, a beta version of the app was deployed to evaluate the useability and effectiveness of data collection. Several issues with the data collection were identified and improvements to the app were implemented. Evaluation of the data and data visualization options will be presented using the most recent version of the application.

Evaluation of a multi-function in-ear device performance in the presence of impulse noise using acoustic test fixtures

Real-time noise exposure monitoring has the potential to assess risk before damage occurs. We present on the development of an in- ear device that (1) provides hearing protection; (2) measures the noise in the ear canal to estimate exposure dose; and (3) monitors distortion product otoacoustic emissions (DPOAE) over time. To validate the device’s ability to measure impulse noise under hearing protection, we measured the response in a field test. A single AR15 was shot multiple times, and four prototypes were used in multiple acoustic test fixtures (ATFs) at different relative distances from the exit of the gun. The ATFs included a G.R.A.S 45 CB in addition to a head simulator designed to detect both air- and bone-conducted sound. We evaluated the impulse peak insertion loss (IPIL) metric of the device inserted by itself or in combination with other hearing protection (“double protection” condition) to assess attenuation of impulsive noises. We also compared the device's microphone measurements to those obtained through the ATF microphones, and to the recordings of external microphones located at or near the ear of each ATF. Results demonstrate the value of testing in-ear devices in acoustic test fixtures during development phases.

A Review on Higher Order Continuous-Time Sigma-Delta Modulators for ADC Design

Abstract—In this paper, the concept of adaptive filtering has been proposed. It has been shown that signals often undergo variation in the noise environment encountered. Hence it becomes mandatory to design filters based on noise shaping and adaptive noise cancellation. Such filters are called adaptive filters and are used for applications requiring real time noise cancellation such as speech processing, live video broadcasting etc. The paper presents the structure of Sigma Delta or MASH ADCs which employ recursive encoding. SDM ADCs are also used to convert high bit-count, low- frequency digital signals into lower bit-count, higher-frequency digital signals as part of the process to convert digital signals into analog as part of a digital-to-analog converter (DAC). The paper focuses on the various approaches used thus far for the purpose, citing the pros and cons of each approach. Keywords— Adaptive Filter Design, Noise Filtering, Sigma Delta Modulator (SDM), MASH, Signal to Noise Ratio.

Road traffic noise exposure assessment based on spatiotemporal data fusion

The current static assessment methods for road traffic noise exposure have limitations in representing spatiotemporal variations and only account for the noise exposure of registered residents. To address these issues, this study proposes an assessment method by fusing spatiotemporal noise and population distributions. This approach estimates spatiotemporal noise distributions through real-time noise monitoring data and an update model of noise source intensity calibrated by historical noise monitoring data and road segment speed data. Meanwhile, spatiotemporal population distributions are generated by aggregating standardized spatiotemporal trajectories of users extracted from mobile phone data. The proposed method is applied to evaluate the population exposure to traffic noise in a Chinese city in different hourly periods. The results demonstrate that this method is a quantitative tool for assessing road traffic noise exposure dynamically, providing policymakers with valuable information to locate hotspots of traffic noise over short periods of time.

Dynamic load altering attack detection based on adaptive fading Kalman filter in smart grid

AbstractThis paper mainly studies a detection method of dynamic load altering attacks (D‐LAAs) in smart grids. First, communication factors are considered, and a smart grid discrete system model under D‐LAA attack is established. Second, for closed‐loop D‐LAAs, an adaptive fading Kalman filter (AFKF) is designed to estimate the states of smart grids with Gaussian noise in real time, and a Euclidean distance ratio detection algorithm based on AFKF is proposed to detect D‐LAAs. Moreover, the proposed detection algorithm can identify D‐LAAs even in the presence of noise in the measurement data, significantly enhancing the speed of attack detection. Finally, take a smart grid with three generators and six buses as an example. Its feasibility and effectiveness of the Euclidean distance ratio detection algorithm are verified by simulations. The simulations are carried out through the real‐time hardware‐in‐the‐loop simulation platform, which is mainly composed of StarSim and multi‐tasking devices.

ANALYSIS AND DEVELOPMENT OF A SMART NOISE INFORMATION COLLECTION SYSTEM BASED ON THE SPECTRUM ANALYZER SVAN 958A

This paper presents the analysis and development of a smart noise information collection system utilizing the Spectrum Analyzer SVAN 958A to enhance real-time noise monitoring and data analysis. The study addresses the limitations of traditional noise measurement tools, which often lack real-time processing and comprehensive integration with modern data management platforms. By leveraging the SVAN 958A’s advanced frequency domain analysis capabilities and integrating it with IoT-based technologies and machine learning algorithms, the proposed system aims to improve noise data accuracy, automation, and scalability. Through the design and implementation of a real-time data processing framework, the system enables precise noise source identification and facilitates immediate response to noise pollution issues. The system’s performance was validated in various urban and industrial noise environments, demonstrating significant improvements in noise data collection accuracy, with enhanced signal differentiation by up to 35% over traditional methods. Additionally, real-time data visualization tools were developed to support regulatory compliance and decision-making processes. The results of this research suggest that the proposed smart noise information collection system can serve as an efficient tool for environmental noise monitoring, offering both practical benefits for public health and potential applications in smart city infrastructure. The system also opens avenues for further research in integrating advanced analytics into acoustic monitoring frameworks, contributing to the ongoing development of smart environmental management technologies.

Real-time Noise Risk Assessment as Preventive Action to Occupational Disease in A Pharmaceutical Industry

Noise is a form of dangerous exposure that occurs around humans because of sound sources from industrial machines, work equipment, alarms, transportation equipment, etc. Continuous exposure to noise with noise levels higher than 85 dBA can lead to psychological and psychological health problems. The aim of this research is to identify the intensity of noise in the pharmaceutical industry and to map the noise to humans. Noise evaluation began by conducting a survey of conditions on the production floor by arranging a production floor layout accompanied by the location of machines and other work facilities. The next step was to determine the size and number of networks that would help in identifying interference measurement points. It took around 100 points to identify interference and then measurements were carried out using a noise meter with 6 repetitions per point. The equivalent continuous sound pressure level (Leq) was calculated first before being used as input to the Surfer software to obtain interference on the production floor. The research results showed that there were 100 points spread across 12 workstations in this drug manufacturing industry. The results showed that there were 19 points that received interference ≥ 85 dBA. The areas with the biggest disturbances were the EPM machine area, spooling machine, and slitting room.

Real-Time Noise Suppression Method Using Deep Learning Algorithm

In this study, we present an innovative approach to real-time facial feature detection utilizing the MTCNN (MultiTask Cascaded Neural Network) deep learning architecture. Unlike traditional methods, our novel framework combines advanced techniques to achieve unparalleled precision and efficiency in facial feature localization. Through a meticulous exploration of MTCNN's capabilities, we unveil a transformative methodology that significantly enhances the speed and accuracy of real-time facial detection. Our research focuses on pushing the boundaries of existing facial recognition technologies, introducing a fresh perspective on the application of MTCNN. By leveraging its unique architecture, we not only address the challenges associated with real-time detection but also enhance the overall robustness of the system. The proposed approach showcases the untapped potential of MTCNN, establishing it as a key player in the realm of facial feature detection. Through rigorous experimentation and evaluation, we demonstrate the superiority of our approach over conventional methods, highlighting its effectiveness in diverse scenarios. This work contributes to the ongoing evolution of deep learning applications in computer vision, with implications for security, surveillance, and various human-computer interaction domains. Our findings open new avenues for researchers and practitioners seeking cutting-edge solutions in the dynamic field of real-time facial feature detection.