Noise Conditions Research Articles

The Impact of Soundscapes on Healthcare Teams: A Literature Review

Healthcare teams often work in complex systems where soundscapes include numerous noise sources and levels which could impact teamwork. While studies have analyzed the impact of sound on patients, articles describing clinician and clinical team experience are relatively sparse. The purpose of this literature review was to determine what is currently known about soundscapes and their impact on teamwork and team performance in hospital-based healthcare settings. Eleven studies met the inclusion criteria. The Input-Process-Output framework and SEIPS 2.0 model guided our review and categorization of the included studies. The work environments studied included the operating room (OR), perioperative area, and a simulation. Noise input was grouped by existing noise, music or controlled sound conditions, and communication wearables during clinical or simulated tasks. Nine of the 11 articles studied the impact sound environment on team communication and three articles measured other team processes. The findings from this review assessed both existing and experimental noise conditions discovering associations with team performance processes and outcomes. Overall, previous studies have found that noise can impact individuals and elements of teamwork, especially communication. Future research is needed to develop best practices and innovative solutions supporting a positive soundscape for teamwork.

Listening effort in children and adults in classroom noise

It is well known that hearing in noisy situations is more challenging than in quiet environments. This holds true for adults and especially for children. This study employed a child-appropriate dual-task paradigm to investigate listening effort in children aged six to ten years and young adults. The primary task involved word recognition, while the secondary task evaluated digit recall. Additionally, subjective perception of listening effort was assessed using a child-appropriate questionnaire. This study incorporated plausible sound reproduction and examined classroom scenarios including multi-talker babble noise with two signal-to-noise ratios (0 dB and −3 dB) in an anechoic and an acoustically simulated classroom environment. Forty-four primary school children aged six to ten (17 first- to second-graders and 18 third- to fourth-graders) and 25 young adults participated in this study. The results revealed differences in listening effort between the noise conditions in third- to fourth-graders and supported using the dual-task paradigm for that age group. For all three age groups, a greater subjective perception of listening effort in noise was found. Furthermore, a correlation between the subjective perception of listening effort and behavioural listening effort based on the experimental results was found for third- to fourth-graders and adults.

Research on improved dynamic load identification method based on Kalman filter under noise interference

As a mathematical approach for solving inverse problems, the dynamic load identification method is particularly useful when directly measuring the load acting on a structure is challenging. The Kalman filter (KF) algorithm is commonly employed for dynamic load identification. However, in practice, measurement noise can significantly affect the accuracy of the KF algorithm's results. In this study, we investigate the impact of response noise on the accuracy of load identification results using the KF algorithm. To eliminate the influence of measurement noise on identification accuracy, we propose an improved dynamic load identification method. This method incorporates adaptive techniques to assess varying levels of response noise and improve the load identification accuracy. Notably, this method is applicable in both physical and modal domains. We conduct numerical simulations using a 5-DOF system under different noise conditions. Compared to traditional dynamic load identification method based on KF algorithm, the improved method consistently achieves superior accuracy in load identification across various noise levels. Moreover, an experiment is conducted to further validate the feasibility of the proposed method. The results showed that the proposed method is effective and can achieve high-precision identification of unknown loads.

Modal identification of wind turbine tower based on optimal fractional order statistical moments

AbstractIn vibration testing of civil engineering structures, the first two vibration modes are crucial in representing the global dynamic behavior of the structure measured. In the present study, a comprehensive method is proposed to identify the first two vibration modes of wind turbine towers, which is based on the analysis of fractional order statistical moments (FSM). This study offers novel contributions in two key aspects: (1) theoretical derivations of the relationship between FSM and vibration mode; and (2) successful use of 32/7‐order displacement statistical moment as the optimal FSM to identify wind turbine tower modes, by combining with noise resistance analysis, sensitivity analysis, and stability analysis, respectively. Using the proposed method, the FSM was first used to identify the modal vibration of wind turbine towers. By obtaining the response of the structure on the same vertical line, FSM was then calculated to estimate the corresponding structural modal vibration. Considering other influencing factors in the field test, the modal identification results of this index under different excitation forms and noise conditions were analyzed based on numerical simulation and verified with field wind tower test data. The results of the evaluation show that the proposed statistical moments of can accurately identify the first two vibration modes of wind turbine towers. This presents a new robust method for modal vibration identification, that is, simple and effective in its implementation.

Computer diffraction tomography: a comparative analysis of the use of controlled and wavelet filters for image processing

The paper provides digital processing of 2D X-ray projection images of a Coulomb-type point defect in a Si(111) crystal recorded by a detector against the background of statistical Gaussian noise. A managed filter and a wavelet filter with a 4th-order Daubechies function are used. The efficiency of filtering 2D images is determined by calculating the relative quadratic deviations of the intensities of filtered and reference (noiseless) 2D images averaged over all points. A comparison of the calculated values of the relative deviations of the intensities shows that the considered methods work quite well and both, in principle, can be effectively used in practice for noise processing of X-ray diffraction images used for 3D reconstruction of nanoscale defects of crystal structures.

Low-dose radiographic inspection of welding by a novel aperiodic reverse stochastic resonance method

Abstract Low-dose radiographic inspection is a growing trend in industry to minimize radiation risks to humans and the environment. However, reduction in radiation dose often introduces significant noise, which affects image quality and hinders accurate identification of subtle defects. This study addresses this issue by introducing a novel phenomenon called aperiodic reverse stochastic resonance (ARSR), observed in nonlinear systems excited by aperiodic binary signals. ARSR enables simultaneous amplitude amplification and reversal of signals under specific noise conditions. Leveraging ARSR, we propose an image denoising framework for low-dose radiographic inspections. First, a set of projection data is obtained by using Radon transform to reduce the dimensionality of X-ray images from different angles. Then, the projection data is modulated based on the ARSR system. Finally, the image is reconstructed based on the inverse Radon transform. Simulations and experimental comparison results in welding applications validate the effectiveness of the framework, demonstrating significant improvements in image quality for low-dose radiographic defect detection. Unlike advanced methods such as Gaussian filtering, BM3D, and DnCNN, which operate at the pixel level, ARSR performs denoising at the projection data stage, reducing noise impact, preserving original information, and focusing on physical data processing during imaging. This approach enhances the detection of subtle defects, highlighting the potential of stochastic resonance in image processing.

Analyzing One- and Two-bit Data to Reduce Memory Requirements for F -statistic-based Gravitational Wave Searches

Searches for continuous-wave gravitational radiation in data collected by modern long-baseline interferometers, such as the Laser Interferometer Gravitational-wave Observatory (LIGO), the Virgo interferometer, and the Kamioka Gravitational Wave Detector, can be memory intensive. A digitization scheme is described that reduces the 64-bit interferometer output to a one- or two-bit data stream while minimizing distortion and achieving considerable reduction in storage and input/output cost. For the representative example of the coherent, maximum-likelihood matched filter known as the F -statistic, it is found using Monte Carlo simulations that the injected signal only needs to be ≈24% stronger (for one-bit data) and ≈6.4% stronger (for two-bit data with optimal thresholds) than a 64-bit signal in order to be detected with 90% probability in Gaussian noise. The foregoing percentages do not change significantly when the signal frequency decreases secularly, or when the noise statistics are not Gaussian, as verified with LIGO Science Run 6 data.

Robust Real-Time Cancer Tracking via Dual-Panel X-Ray Images for Precision Radiotherapy

Respiratory-induced tumor motion presents a critical challenge in lung cancer radiotherapy, potentially impacting treatment precision and efficacy. This study introduces an innovative, deep learning-based approach for real-time, markerless lung tumor tracking utilizing orthogonal X-ray projection images. It incorporates three key components: (1) a sophisticated data augmentation technique combining a hybrid deformable model with 3D thin-plate spline transformation, (2) a state-of-the-art Transformer-based segmentation network for precise tumor boundary delineation, and (3) a CNN regression network for accurate 3D tumor position estimation. We rigorously evaluated this approach using both patient data from The Cancer Imaging Archive and dynamic thorax phantom data, assessing performance across various noise levels and comparing it with current leading algorithms. For TCIA patient data, the average DSC and HD95 values were 0.9789 and 1.8423 mm, respectively, with an average centroid localization deviation of 0.5441 mm. On CIRS phantoms, DSCs were 0.9671 (large tumor) and 0.9438 (small tumor) with corresponding HD95 values of 1.8178 mm and 1.9679 mm. The 3D centroid localization accuracy was consistently below 0.33 mm. The processing time averaged 90 ms/frame. Even under high noise conditions (S2 = 25), errors for all data remained within 1 mm with tracking success rates mostly at 100%. In conclusion, the proposed markerless tracking method demonstrates superior accuracy, noise robustness, and real-time performance for lung tumor localization during radiotherapy. Its potential to enhance treatment precision, especially for small tumors, represents a significant step toward improving radiotherapy efficacy and personalizing cancer treatment.

Robust Trend Analysis in Environmental Remote Sensing: A Case Study of Cork Oak Forest Decline

We introduce a novel methodological framework for robust trend analysis (RTA) using remote sensing data to enhance the accuracy and reliability of detecting significant environmental trends. Our approach sequentially integrates the Theil–Sen (TS) slope estimator, the Contextual Mann–Kendall (CMK) test, and the false discovery rate (FDR) control. This comprehensive method addresses common challenges in trend analysis, such as handling small, noisy datasets with outliers and issues related to spatial autocorrelation, cross-correlation, and multiple testing. We applied this RTA workflow to study tree cover trends in Los Alcornocales Natural Park (Southern Spain), Europe’s largest cork oak forest, analysing interannual changes in tree cover from 2000 to 2022 using Terra MODIS MOD44B data. Our results reveal that the TS estimator provides a robust measure of trend direction and magnitude, but its effectiveness is dramatically enhanced when combined with the CMK test. This combination highlights significant trends and effectively corrects for spatial autocorrelation and cross-correlation, ensuring that genuine environmental signals are distinguished from statistical noise. Unlike previous workflows, our approach incorporates the FDR control, which successfully filtered out 29.6% of false discoveries in the case study, resulting in a more stringent assessment of true environmental trends captured by multi-temporal remotely sensed data. In the case study, we found that approximately one-third of the area exhibits significant and statistically robust declines in tree cover, with these declines being geographically clustered. Importantly, these trends correspond with relevant changes in tree cover, emphasising the ability of RTA to detect relevant environmental changes. Overall, our findings underscore the crucial importance of combining these methods, as their synergy is essential for accurately identifying and confirming robust environmental trends. The proposed RTA framework has significant implications for environmental monitoring, modelling, and management.

Network mutual information measures for graph similarity

A wide range of tasks in network analysis, such as clustering network populations or identifying anomalies in temporal graph streams, require a measure of the similarity between two graphs. To provide a meaningful data summary for downstream scientific analyses, the graph similarity measures used for these tasks must be principled, interpretable, and capable of distinguishing meaningful overlapping network structure from statistical noise at different scales of interest. Here we derive a family of graph mutual information measures that satisfy these criteria and are constructed using only fundamental information theoretic principles. Our measures capture the information shared among networks according to different encodings of their structural information, with our mesoscale mutual information measure allowing for network comparison under any specified network coarse-graining. We test our measures in a range of applications on real and synthetic network data, finding that they effectively highlight intuitive aspects of network similarity across scales in a variety of systems.

A Finite-Time Path Tracking Control Scheme for Unmanned Vehicles Based on Multidimensional Taylor Network and Adaptive Filtering

Aiming at the path tracking control problem of unmanned vehicles under the conditions of model uncertainty and measurement noise, a finite-time path tracking control scheme based on multidimensional Taylor network (MTN) is proposed. The MTN model is used to characterize the uncertainty of the unmanned vehicle model, and the improved back propagation (BP) algorithm is used as its learning algorithm; an adaptive MTN filter is designed to filter out the measurement noise, MTN is used as a filter, and the least mean square (LMS) algorithm is used as its learning algorithm; an MTN finite-time controller is designed to perform precise path tracking control on the unmanned vehicle, which can quickly and accurately track the reference path; according to the finite-time control theory, the convergence proof verifies the effectiveness of the proposed method through unmanned vehicle simulation experiments.

Impact of Mask Type as Training Target for Speech Intelligibility and Quality in Cochlear-Implant Noise Reduction

The selection of a target when training deep neural networks for speech enhancement is an important consideration. Different masks have been shown to exhibit different performance characteristics depending on the application and the conditions. This paper presents a comprehensive comparison of several different masks for noise reduction in cochlear implants. The study incorporated three well-known masks, namely the Ideal Binary Mask (IBM), Ideal Ratio Mask (IRM) and the Fast Fourier Transform Mask (FFTM), as well as two newly proposed masks, based on existing masks, called the Quantized Mask (QM) and the Phase-Sensitive plus Ideal Ratio Mask (PSM+). These five masks are used to train networks to estimate masks for the purpose of separating speech from noisy mixtures. A vocoder was used to simulate the behavior of a cochlear implant. Short-time Objective Intelligibility (STOI) and Perceptual Evaluation of Speech Quality (PESQ) scores indicate that the two new masks proposed in this study (QM and PSM+) perform best for normal speech intelligibility and quality in the presence of stationary and non-stationary noise over a range of signal-to-noise ratios (SNRs). The Normalized Covariance Measure (NCM) and similarity scores indicate that they also perform best for speech intelligibility/gauging the similarity of vocoded speech. The Quantized Mask performs better than the Ideal Binary Mask due to its better resolution as it approximates the Wiener Gain Function. The PSM+ performs better than the three existing benchmark masks (IBM, IRM, and FFTM) as it incorporates both magnitude and phase information.

A low noise 3×3 coupler demodulation scheme based on spectral subtraction with multi-window minimum statistical noise estimation

A low noise 3×3 coupler demodulation scheme based on spectral subtraction with multi-window minimum statistical noise estimation

Directionality in BiCROS hearing aids: an investigation of objective and subjective outcomes

Objectives To assess the effect of forward and omnidirectional microphone configurations in BiCROS versus monaural hearing aids on objective and subjective outcomes in different noise conditions. Design After fitting and a 4-week acclimatisation period, speech recognition and sound quality were measured using forward directional, omnidirectional, and unaided settings. Two noise configurations were used, surrounding noise and noise presented from the aided (better) ear. Subjective outcomes were assessed using the SSQ-b and BBSS questionnaires and participant interviews. Study sample Eighteen adult participants (mean: 74.6 y; range: 61–94 y; ten males, eight females) with mild to moderately severe SNHL in their better ear (PTA0.5–4khz > 20 dB HL) and limited usable hearing in their poorer ear (average PTA0.5–4khz > 100 dB HL). Results Significant improvement in speech recognition and sound quality for BiCROS and monaural directional settings over omnidirectional and unaided, in both noise configurations. There were no significant differences observed between monoaural and BiCROS directional settings. Conclusion Speech in noise recognition and sound quality scores demonstrated a significant directional benefit for both BiCROS and monaural directional fitting settings over omnidirectional and unaided conditions. Unique BiCROS-specific experiences were identified in a patient-oriented approach. These can inform the development of BiCROS-tailored tools.

Transformer assisted by DSC and BiLSTM for bearing fault pattern recognition under strong noise interference

ABSTRACT Deep learning has greatly promoted intelligent fault diagnosis for bearings and has led to the development of many intelligent fault recognition methods based on deep neural networks. However, bearing fault signals in industrial applications are susceptible to noise interference, and intelligent recognition methods for bearing faults under strong noise interference are lacking. An intelligent fault pattern recognition model termed IFPR-MDL, which is based on a Transformer assisted by depthwise separable convolutional (DSC) and bidirectional long short-term memory (BiLSTM) networks, is designed to identify bearing faults under strong noise conditions. In this model, multiscale features are first extracted by a multiscale feature extraction module designed on the basis of DSC; then, contextual information is further extracted by a BiLSTM, and information from different scales is integrated by Transformer encoders. The DSC network facilitates model parameter reduction, the BiLSTM network has expertise in mining temporal features, and the Transformer network excels at capturing long-range feature correlations. This model fully utilises the advantages of these models and complements them to improve the anti-interference ability of bearing fault pattern recognition. Tests on several public datasets show that the proposed model has higher recognition accuracy, stronger generalisation ability, and noise resistance in strong noise situations.

Non-Markovian noise mitigation in quantum teleportation: enhancing fidelity and entanglement

Maintaining quantum coherence and entanglement in the presence of environmental noise, particularly within non-Markovian contexts, represents a significant challenge for the progression of quantum information science and technology. This study offers a substantial advancement by investigating the dynamics of a two-qubit system subjected to diverse noise conditions, encompassing relaxation, dephasing, and their cumulative effects. By employing quantum-state-diffusion equations specifically crafted for non-Markovian environments, we introduce an innovative strategy to counteract the detrimental influences of environmental noise on quantum teleportation fidelity and entanglement concurrence. Our results underscore the potential for external interventions to markedly improve the resilience of quantum information processing tasks over prolonged durations, especially in settings where dephasing noise prevails. A key revelation is the intricate relationship between dephasing noise and the initial state of entanglement, which profoundly impacts the occurrence of entanglement sudden death. This research not only deepens our comprehension of quantum system dynamics under noisy circumstances but also furnishes practical directives for engineering robust quantum systems, a necessity for the development of scalable quantum technologies.

Modeling uncertainty in P-wave arrival-times retrieved from DAS data: case-studies from 15 fiber optic cables

Summary Distributed Acoustic Sensing (DAS) technology enables the detection of waves generated by seismic events, generally as uniaxial strain/strain rate time series observed for dense, subsequent, portions of a Fiber Optic Cable (FOC). Despite the advantages in measurement density, data quality is often affected by uniaxial signal polarization, site effects and cable coupling, beyond the physical energy decay with distance. To better understand the relative importance of these factors for data inversion, we attempt a first modeling of noise patterns affecting DAS arrival times for a set of seismic events. The focus is on assessing the impact of noise statistics, together with the geometry of the problem, on epicentral location uncertainties. For this goal, we consider 15 ”real-world” cases of DAS arrays with different geometry, each associated with a seismic event of known location. We compute synthetic P-wave arrival times and contaminate them with four statistical distributions of the noise. We also estimate P-wave arrival times on real waveforms using a standard seismological picker. Eventually, these five datasets are inverted using a Markov chain Monte Carlo (McMC) method, which offers the evaluation of the relative event location differences in terms of Posterior Probability Density (PPD). Results highlight how cable geometry influences the shape, extent, and directionality of the PPDs. However, synthetic tests demonstrate how noise assumptions on arrival times often have important effects on location uncertainties. Moreover, for half of the analyzed case studies, the observed and synthetic locations are more similar when considering noise sources that are independent of the geometrical characteristics of the arrays. Thus, the results indicate that axial polarization, site conditions, and cable coupling, beyond other intrinsic features (e.g., optical noise), are likely responsible for the complex distribution of DAS arrival times. Overall, the noise sensitivity of DAS suggests caution when applying geometry-only-based approaches for the a priori evaluation of novel monitoring systems.

Research on ECG signal reconstruction based on improved weighted nuclear norm minimization and approximate message passing algorithm.

In order to improve the energy efficiency of wearable devices, it is necessary to compress and reconstruct the collected electrocardiogram data. The compressed data may be mixed with noise during the transmission process. The denoising-based approximate message passing (AMP) algorithm performs well in reconstructing noisy signals, so the denoising-based AMP algorithm is introduced into electrocardiogram signal reconstruction. The weighted nuclear norm minimization algorithm (WNNM) uses the low-rank characteristics of similar signal blocks for denoising, and averages the signal blocks after low-rank decomposition to obtain the final denoised signal. However, under the influence of noise, there may be errors in searching for similar blocks, resulting in dissimilar signal blocks being grouped together, affecting the denoising effect. Based on this, this paper improves the WNNM algorithm and proposes to use weighted averaging instead of direct averaging for the signal blocks after low-rank decomposition in the denoising process, and validating its effectiveness on electrocardiogram signals. Experimental results demonstrate that the IWNNM-AMP algorithm achieves the best reconstruction performance under different compression ratios and noise conditions, obtaining the lowest PRD and RMSE values. Compared with the WNNM-AMP algorithm, the PRD value is reduced by 0.17∼4.56, the P-SNR value is improved by 0.12∼2.70.

The predictive power of Dutch end of primary school tests for educational attainment in ninth grade

AbstractDutch secondary education is a multi‐tiered system, and many students are placed in a single track in the first year (seventh grade) of secondary education. As part of the placement procedure, all sixth‐grade students take an end of primary school test. Although these standardised attainment tests (SATs) are considered high‐stakes tests, the predictive accuracy of many of these SATs has not been studied comprehensively. In the current study, three SATs commonly used in The Netherlands (CET, IEP and R8) were compared on their predictive accuracy of educational attainment in ninth grade. The predictive accuracy was also investigated for particular track recommendations, using multiple cohorts of population data, each of around 160,000 students. For the two most recent cohorts, CET was overall a more accurate predictor of educational attainment in ninth grade than R8, which in turn was a more accurate predictor than IEP. All three attainment tests had only mediocre to acceptable predictive power overall. Furthermore, all three tests had very poor predictive power for certain single‐track recommendations. For these track recommendations, many students attained a lower or higher educational level in ninth grade. This study is the first to compare the predictive accuracy of these SATs. While statistical noise is normal when using tests, since different tests are used that vary widely in predictive accuracy, there is much more noise than is acceptable. As long as the predictive accuracy is poor to mediocre, these SATs should not have a large role to play in the Dutch placement procedure for secondary education.

Revolutionizing swarm dynamics: the role of receptive fields in enhancing convergence and stability

The classic Vicsek model, while influential in understanding swarm behavior, has limitations in achieving motion consensus and convergence speed, especially under varying conditions of density and noise. This study aims to introduce a novel receptive field mechanism to the Vicsek model to enhance its performance in terms of motion consensus and convergence speed within swarms. The modified model divides a particle’s surrounding area into excitation and inhibition zones based on distinct functions. This structural modification is designed to enrich evolutionary behavior and improve consensus convergence capabilities. Experimental outcomes indicate that the proposed model achieves faster convergence rates towards motion consensus under various density and noise conditions compared to traditional models. Specifically, while classic Vicsek models fail to converge to an overall polarization state under high noise levels and exhibit quasi-periodic oscillations, the enhanced model demonstrates stable convergence without oscillatory behavior across both low- and high-noise environments. The findings highlight the superior evolutionary consistency characteristics of the improved model, offering new theoretical and practical insights into the stability and controllability of swarms. This advancement presents significant implications for the development of more robust swarm systems.