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Objective: This study aimed to evaluate the significance of background noise in machine learning models assessing the GRBAS scale for voice disorders., Methods: A dataset of 1406 voice samples was collected from retrospective data, and a 5-layer 1D convolutional neural network (CNN) model was constructed using TensorFlow. The dataset was divided into training, validation, and test data. Gaussian noise was added to test samples at various intensities to assess the model's noise resilience. The model's performance was evaluated using accuracy, F1 score, and quadratic weighted Cohen's kappa score., Results: The model's performance on the GRBAS scale generally declined with increasing noise intensities. For the G scale, accuracy dropped from 70.9% (original) to 8.5% (at the highest noise), F1 score from 69.2% to 1.3%, and Cohen's kappa from 0.679 to 0.0. Similar declines were observed for the remaining RBAS components., Conclusion: The model's performance was affected by background noise, with substantial decreases in evaluation metrics as noise levels intensified. Future research should explore noise-tolerant techniques, such as data augmentation, to improve the model's noise resilience in real-world settings., Level of Evidence: This study evaluates a machine learning model using a single dataset without comparative controls. Given its non-comparative design and specific focus, it aligns with Level 4 evidence (Case-series) under the 2011 OCEBM guidelines Laryngoscope, 134:3537-3541, 2024., (© 2024 The Authors. The Laryngoscope published by Wiley Periodicals LLC on behalf of The American Laryngological, Rhinological and Otological Society, Inc.)

Objectives: Understanding speech in noise (SIN) is the dominant complaint of individuals with hearing loss. For decades, the default test of speech perception in routine audiologic assessment has been monosyllabic word recognition in quiet (WRQ), which does not directly address patient concerns, leading some to advocate that measures of SIN should be integrated into routine practice. However, very little is known with regard to how SIN abilities are affected by different types of hearing loss. Here, we examine performance on clinical measures of WRQ and SIN in a large patient base consisting of a variety of hearing loss types, including conductive (CHL), mixed (MHL), and sensorineural (SNHL) losses., Design: In a retrospective study, we examined data from 5593 patients (51% female) who underwent audiometric assessment at the Stanford Ear Institute. All individuals completed pure-tone audiometry, and speech perception testing of monaural WRQ, and monaural QuickSIN. Patient ages ranged from 18 to 104 years (average = 57). The average age in years for the different classifications of hearing loss was 51.1 (NH), 48.5 (CHL), 64.2 (MHL), and 68.5 (SNHL), respectively. Generalized linear mixed-effect models and quartile regression were used to determine the relationship between hearing loss type and severity for the different speech-recognition outcome measures., Results: Patients with CHL had similar performance to patients with normal hearing on both WRQ and QuickSIN, regardless of the hearing loss severity. In patients with MHL or SNHL, WRQ scores remained largely excellent with increasing hearing loss until the loss was moderately severe or worse. In contrast, QuickSIN signal to noise ratio (SNR) losses showed an orderly systematic decrease as the degree of hearing loss became more severe. This effect scaled with the data, with threshold-QuickSIN relationships absent for CHL, and becoming increasingly stronger for MHL and strongest in patients with SNHL. However, the variability in these data suggests that only 57% of the variance in WRQ scores, and 50% of the variance in QuickSIN SNR losses, could be accounted for by the audiometric thresholds. Patients who would not be differentiated by WRQ scores are shown to be potentially differentiable by SIN scores., Conclusions: In this data set, conductive hearing loss had little effect on WRQ scores or QuickSIN SNR losses. However, for patients with MHL or SNHL, speech perception abilities decreased as the severity of the hearing loss increased. In these data, QuickSIN SNR losses showed deficits in performance with degrees of hearing loss that yielded largely excellent WRQ scores. However, the considerable variability in the data suggests that even after classifying patients according to their type of hearing loss, hearing thresholds only account for a portion of the variance in speech perception abilities, particularly in noise. These results are consistent with the idea that variables such as cochlear health and aging add explanatory power over audibility alone., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2024 The Authors. Ear & Hearing is published on behalf of the American Auditory Society, by Wolters Kluwer Health, Inc.)

In the evolving domain of High Dynamic Range (HDR) display metrology, the precise definition and systematic characterization of input signals is important. Using input signals that include both spatially and temporally complex image sequences is beneficial to reflect properties of real‐world content as well as to cover corner cases. One challenge with using this kind of content is to verify if it is indeed suitable for the intended purpose. This paper introduces an automated framework employing a set of statistical measures to define and characterize content, which then can serve as an important steppingstone towards programmatic image sequences and testing workflows. [ABSTRACT FROM AUTHOR]

Purpose: The Listen to Learn for Life (L3) Assessment Framework is a framework that outlines how to systematically assess the links between the classroom environment, listening, learning and well-being. The purpose of this paper is to review the published literature to understand what is known about the characterisation of activity component of the L3 Assessment Framework and determine what is not known and needs to be investigated in future research. Design/methodology/approach: Literature reviews (Web searches for systematic, scoping or general reviews; scoping reviews following the preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews protocol; and extended Web searches) were conducted to characterise lecture, group work and independent work activities in terms of the perceptual setting, source degradation and transmission degradation. Findings: Most research that has been conducted is for classrooms in general and does not specify results specifically for lecture, group work and independent work, which is important for understanding how the learning environment changes with the different activities. What is known about the lecture, group work and independent work activities are discussed; however, it is noted that this often comes from only a few studies. Future research areas to contribute to this literature as well as fill current research gaps are proposed. Originality/value: To the best of the authors' knowledge, this paper is the first review paper to synthesize previous research characterising the classroom environment for different activities using the L3 Assessment Framework. It provides an analysis of the limitations of existing literature and proposes future research to help fill in these gaps. [ABSTRACT FROM AUTHOR]

Reorganization of neonatal intensive care by introducing clinical microsystems may help to allocate nursing time more appropriately to the needs of patients. However, there is concern that cohorting infants according to acuity may enhance noise levels. This single-center study investigated the impact of reorganization of neonatal intensive care unit by implementing clinical microsystems in a Level III NICU on environmental noise. This prospective study measured 24-h noise levels over a period of 6 months during pre- and post-implementation of microsystems cohorting infants of similar acuity. Comparative analyses of the mixed acuity (i.e., before) and the cohorting (i.e., after) model were performed by creating daily profiles from continuous noise level measurements and calculating the length of exposure to predefined noise levels. Compared to baseline daytime measurements, noise levels were 3-6 dBA higher during physician handover. Noise levels were 2-3 dBA lower on weekends and 3-4 dBA lower at night, independent of the organizational model. The introduction of clinical microsystems slightly increased average noise levels for high-acuity pods (A and B) but produced a much more substantial decrease for low-acuity pods (E), leading to an overall reduction in unit-wide noise levels.    Conclusion: Our data show that noise levels are more driven by human behavior than by technical devices. Implementation of microsystems may help to reduce noise exposure in the lower acuity pods in a NICU. What is Known: • Excessive noise levels can lead to adverse effects on the health and development of premature infants and other critically ill newborns. • The reorganization of the neonatal intensive care unit following the clinical microsystems principles might improve quality of care but also affect noise exposure of staff and patients. What is New: • The transition from a mixed -acuity to cohorting model is associated with an overall reduction in noise levels, particularly in low-acuity pods requiring less nursing care. • Nevertheless, baseline noise levels in both models exceeded the standard permissible limits., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

We propose deep learning‐enhanced self‐interference incoherent digital holography. Self‐interference incoherent digital holography is a hologram acquisition technique under the incoherent light condition, which can capture the real‐world complex hologram. To overcome unwanted optical defects of the self‐interference, we suggest a data‐driven approach for reducing the additional noise and enhancing the image quality. [ABSTRACT FROM AUTHOR]

The Perona-Malik (P-M) model exhibits deficiencies such as noise amplification, new noise introduction, and significant gradient effects when processing noisy images. To address these issues, this paper proposes an image-denoising algorithm, ACE-GPM, which integrates an Automatic Color Equalization (ACE) algorithm with a gradient-adjusted P-M model. Initially, the ACE algorithm is employed to enhance the contrast of low-light images obscured by fog and noise. Subsequently, the Otsu method, a technique to find the optimal threshold based on between-class variance, is applied for precise segmentation, enabling more accurate identification of different regions within the image. After that, distinct gradients enhance the image's foreground and background via an enhancement function that accentuates edge and detailed information. The denoising process is finalized by applying the gradient P-M model, employing a gradient descent approach to further emphasize image edges and details. Experimental evidence indicates that the proposed ACE-GPM algorithm not only elevates image contrast and eliminates noise more effectively than other denoising methods but also preserves image details and texture information, evidenced by an average increase of 0.42 in the information entropy value. Moreover, the proposed solution achieves these outcomes with reduced computational resource expenditures while maintaining high image quality. [ABSTRACT FROM AUTHOR]

In response to the challenges of accurately identifying event triggers and elements in long texts related to aquaculture, existing models struggle to differentiate between elements and triggers, as well as effectively recognize complete entity texts. To tackle this issue, this study proposes an algorithm for extracting fish disease events based on pseudo triggers and augmented event element data. The method starts by constructing pseudo samples using the original dataset. Two types of noise datasets are then generated: a trigger noise dataset constructed based on fish disease triggers and an entity noise dataset with varying levels of entity noise constructed based on fish disease entities. Next, three parallel neural networks are deployed to extract sample features from these datasets. The fish disease event extraction for the source dataset employs multi-label classification. For the trigger noise dataset, the sample features are activated using the sigmoid function, and the MRSE loss is utilized for optimization of this branch. For the entity noise dataset, the sample features are activated using the Relu function, and the XOR loss is used for optimization. Finally, the losses from the three branches are combined with weighted summation to obtain the fusion loss. The experimental results on the fish disease dataset used in this paper show that the proposed algorithm achieves an average accuracy of 78.71%, 78.95%, and 79.43% on F1, recall, and precision, respectively, which is a maximum improvement of 11.201%, 11.849%, and 12.421% in accuracy with respect to the baseline model on F1, recall, and precision, respectively. [ABSTRACT FROM AUTHOR]

Publicly available ionograms are often in the form of pictures. This paper proposes a novel algorithm for extracting and repairing the F layer traces from pictorial ionograms. Extensive efforts have been invested in ionogram autoscaling and critical parameter identification to improve the efficiency of scaling algorithms. To obtain the parameters of the F layer automatically, it is necessary to accurately extract the F layer trace. However, research on F layer trace extraction with repair is relatively limited. The method employed in this study makes full use of the characteristics of different types of echoes on the ionograms, and the procedure includes noise preprocessing, coupling noise processing, and trace repair. To enhance the applicability of the repair, two different automatic filling algorithms are adopted to repair the F layer trace. The aim of this paper is to present an adaptive algorithm to automatically extract and repair F layer traces from different pictorial ionograms. The results of Hainan Fuke ionograms illustrate the reliability of the F layer trace extraction and trace repair. [ABSTRACT FROM AUTHOR]

The edge detection method based on the magnetic gradient tensor data plays an important role in magnetic exploration because it is free from geomagnetic interference and contains more abundant information. This paper proposes a new anomaly edge detection method using the magnetic gradient tensor components. The model is established to compare with other methods, such as directional total horizontal derivative (THDz), analytical signal (AS), tilt angle, theta map, and so on, under conditions of vertical magnetization, oblique magnetization, and noise interference. Through the study of the anomaly distribution of the rectangular model, it is observed that the edge detection method proposed in this paper is nearly impervious to noise interference, exhibits strong anti-interference capabilities, delivers a high-quality boundary identification effect, and provides greater accuracy in anomaly edges with minimal error. When multiple anomalous bodies are present, the edge detection results are less susceptible to interference from each other, resulting in higher resolution. The efficiency of the algorithm is demonstrated by real magnetic data from some study areas in Jiangxi Province, China. The experimental results show that the proposed method is more precise and accurate than the total horizontal derivative, analytical signal, tilt angle, and theta map methods. [ABSTRACT FROM AUTHOR]

It is a distinct privilege to produce this article to honor Professor Tam, the foremost authority on jet aeroacoustics. The fundamental characteristics of jet noise have been studied for 70 years, since the pioneering work of Lighthill in the 1950s. The acoustic analogy, with many variants, has served as the leading theory for nearly 50 years. Many leading researchers in the 1970s formulated theories to interpret the measured trends from subsonic and supersonic jets, using acoustic analogy and flow features as the framework. Quadrupoles, dipoles and monopoles were believed to constitute the sources of noise. The discovery of large-scale organized structures in free shear layers and jets sparked a different avenue of thinking about their importance for noise generation. Tam was the first to clearly demonstrate that these structures are efficient generators of noise and constitute the dominant noise sources, especially in the downstream direction. Now, two schools of thought emerged on the sources of jet noise. Experimental measurements showed that the mean flow as well as the turbulence statistics exhibit a self-similarity in the mixing layer and another similarity in the fully developed jet. Based on these observations, Tam proposed that since noise is generated by the turbulence of the jet, the noise spectra generated by fine-scale and large-scale turbulence should also exhibit self-similarity. By examining a large set of supersonic jet noise data acquired at NASA Langley, Tam offered evidence that the turbulent mixing noise of high-speed jets does consist of two independent self-similar components. In this paper, experimental evidence is compiled from an extensive database that quantifies the effect of several parameters that affect jet spectra. A new scaling method is developed and extended to noise predictions for realistic dual-stream nozzle geometries. The objectives of this paper are to serve as a synthesis of noise characteristics and to focus on application to real-world problems. Results from five different experimental measurements are examined: (1) farfield spectral characteristics; (2) azimuthal and polar correlations in the farfield; (3) correlations of jet turbulence fluctuations and farfield sound; (4) measurement of source distributions with an elliptic mirror; and (5) space-time correlation measurements in the nearfield with a cage array, and nearfield-farfield correlations. Two distinctly different trends are observed in the angular ranges of 50° – ∼120° and ∼120° – 165° for all the parameters investigated with the above five approaches. The salient observations are mutually supporting, and the cumulative weight lends credence to the proposition that there are two distinct sources of turbulent mixing noise. [ABSTRACT FROM AUTHOR]

A preceding paper [M. Dhar, J. A. Dickinson, and M. A. Berg, J. Chem. Phys. 159, 054110 (2023)] shows how to remove additive noise from an experimental time series, allowing both the equilibrium distribution of the system and its Green's function to be recovered. The approach is based on nonlinear-correlation functions and is fully nonparametric: no initial model of the system or of the noise is needed. However, single-molecule spectroscopy often produces time series with either photon or photon-counting noise. Unlike additive noise, photon noise is signal-size correlated and quantized. Photon counting adds the potential for bias. This paper extends noise-corrected-correlation methods to these cases and tests them on synthetic datasets. Neither signal-size correlation nor quantization is a significant complication. Analysis of the sampling error yields guidelines for the data quality needed to recover the properties of a system with a given complexity. We show that bias in photon-counting data can be corrected, even at the high count rates needed to optimize the time resolution. Using all these results, we discuss the factors that limit the time resolution of single-molecule spectroscopy and the conditions that would be needed to push measurements into the submicrosecond region. [ABSTRACT FROM AUTHOR]

Image processing has widespread uses practically in every branch of science and arts. Processing images is more difficult than processing sound or data as there are more bits in the high pixel quality image. It requires more space to store the image, more bandwidth to transmit it, and more time and resources to process. An image's complexity may decrease if its bit size is decreased. Sigma-delta modulation, or SDM for short, is an alternative method of minimizing data-word length to compression. Digital signal processing (DSP) systems can be made simpler by using the SDM approach, which was first created for analog to digital conversion (ADC). This paper suggests a novel way to use SDM in MATLAB for improved image processing. Consequently, the suggested single-bit SDM-based image arithmetic architecture is tested and compared with the traditional image arithmetic techniques. Additionally, to see the noisy channel influence on the traditional and proposed systems, some statistical metrics are also studied at different noise variance values, such as signal to noise ratio (SNR), mean square error (MSE), and Peak SNR value. The suggested architecture for the SDM-based image arithmetic precisely matches the addition and subtraction results of the conventional design, even yielding a higher SNR and the same Peak SNR as the traditional methods. In contrast, the outcomes of division and multiplication fall within an acceptable range. For better results the over-sampling ratio (OSR), an inherent characteristic of SDM must be increased at the cost of more processing cycles. Therefore, the trade-off between fewer resources, limited transmission bandwidth, and comparatively more cycles is provided by the SDM-based technique. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to systematically map research on the effect of classroom acoustics and noise on high school students' listening, learning and well-being, as well as identify knowledge gaps to inform future research. Design/methodology/approach: This scoping review followed the PRISMA-ScR protocol. A comprehensive search of four online databases (ERIC, PubMed, Scopus and Web of Science) was conducted. Peer-reviewed papers were included if they conducted a study on the effect of classroom acoustics or noise on students' listening, learning or well-being; had a clear definition of the noise level measurement; were conducted with high school students; and had the full text in English available. Findings: In total, 14 papers met the criteria to be included in the review. The majority of studies assessed the impact of noise on students' listening, learning or well-being. Overall, the results showed that higher noise levels have a negative effect on students' listening, learning and well-being. Effects were even more pronounced for students who were non-native speakers or those with special educational needs such as hearing loss. Therefore, it would be beneficial to limit unnecessary noise in the classroom as much as possible through acoustic insulation, acoustic treatment and classroom management strategies. Originality/value: This paper is the first review paper to synthesize previous research on the effect of classroom acoustics and noise on high school students' listening, learning and well-being. It provides an analysis of the limitations of existing literature and proposes future research to help fill in these gaps. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Atomic & Molecular Physics (1000-0364) is the property of Journal of Atomic & Molecular Physics Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Multifactor uncertain differential equations are powerful tools for studying dynamic systems under multi-source noise. A key challenge in this study is how to accurately estimate unknown parameters based on the framework of uncertainty theory in multi-source noise environments. To address this core problem, this paper innovatively proposes a least-squares estimation method. The essence of this method lies in constructing statistical invariants with a symmetric uncertainty distribution based on observational data and determining specific parameters by minimizing the distance between the population distribution and the empirical distribution of the statistical invariant. Additionally, two numerical examples are provided to help readers better understand the practical operation and effectiveness of this method. In addition, we also provide a case study of JD.com's stock prices to illustrate the advantages of the method proposed in this paper, which not only provides a new idea and method for addressing the problem of dynamic system parameter estimation but also provides a new perspective and tool for research and application in related fields. [ABSTRACT FROM AUTHOR]

The paper presents the noise analysis of a MEMS and ASIC readout integrated circuit (ROIC) constituting the accelerometer developed in the frame of the InnoReh project, aiming at the development of methods for monitoring patients with imbalance disorders. Several experiments were performed at different temperatures and in different configurations: ROIC alone, ROIC with emulated parasitic capacitances, MEMS and ROIC in separate packages, and MEMS and ROIC in a single package. Many noise/interference sources were considered. The results obtained experimentally were compared to the results of theoretical investigations and were within the same order of magnitude, although in practice, the observed noise was always greater than the theoretical estimation. The paper also includes an in-depth analysis to explain these differences. Moreover, it is argued that, in terms of noise, the MEMS sensing element, and not the ROIC, is the quality-limiting factor. [ABSTRACT FROM AUTHOR]

Signal‐to‐noise ratio and spatial resolution are quantitatively analysed in the context of in‐line (propagation based) X‐ray phase‐contrast imaging. It is known that free‐space propagation of a coherent X‐ray beam from the imaged object to the detector plane, followed by phase retrieval in accordance with Paganin's method, can increase the signal‐to‐noise in the resultant images without deteriorating the spatial resolution. This results in violation of the noise‐resolution uncertainty principle and demonstrates 'unreasonable' effectiveness of the method. On the other hand, when the process of free‐space propagation is performed in software, using the detected intensity distribution in the object plane, it cannot reproduce the same effectiveness, due to the amplification of photon shot noise. Here, it is shown that the performance of Paganin's method is determined by just two dimensionless parameters: the Fresnel number and the ratio of the real decrement to the imaginary part of the refractive index of the imaged object. The relevant theoretical analysis is performed first, followed by computer simulations and then by a brief test using experimental images collected at a synchrotron beamline. More extensive experimental tests will be presented in the second part of this paper. [ABSTRACT FROM AUTHOR]

The phenomenon of multiple tones is a typical feature of high-lift configuration noise, and the underlying nonlinear and nonstationary features need to be revealed through time-frequency analysis. This paper introduces the Hilbert–Huang transform method to detect the inherent amplitude modulation and frequency modulation phenomena of multiple tones from 30P30N three-element high-lift configuration with both slat and flap completely stowed. The acoustic modes are extracted from the multiple tones firstly and then the variation features of amplitude and instantaneous frequency are analyzed. The results show that the acoustic energy concentrates on the primary mode with much larger amplitude. Moreover, both the amplitude and instantaneous frequency are periodically varied in time and the predicted modulation frequencies are equal to the frequency intervals between nearby tones, confirming that the inherent temporal features of multiple tones are amplitude modulation and frequency modulation. [ABSTRACT FROM AUTHOR]

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Current research on noise and vibration control of high-speed maglev trains pays more attention to far-field noise, while the level of interior noise has a direct impact on the ride comfort and should be placed equal weight on. In this paper, inter-coach space, one of the main pressure fluctuation sources of a specific type of high-speed maglev train with a design speed of 600 km·h−1, is taken as the research object. The turbulent and acoustic components of wall pressure fluctuations (WPF) are separated based on a wavenumber-frequency analysis approach, and then each component is applied as different forms of source input to the vibroacoustic model, namely, finite element method-boundary element method (FEM-BEM) and statistical energy analysis (SEA) for low- and high-frequency ranges respectively, to investigate the contribution of both components to interior acoustic cavity in all frequency range. It can be seen quantitatively from the results that the amplitude of turbulent component is generally much higher than that of the acoustic one, but it can be vice versa when it comes to the interior response. The conclusion drawn in this paper are able to provide guidance for future researches on more targeted interior noise control of high-speed maglev trains. [ABSTRACT FROM AUTHOR]

Background noises are usually treated as redundant or even harmful to voice conversion. Therefore, when converting noisy speech, a pretrained module of speech separation is usually deployed to estimate clean speech prior to the conversion. However, this can lead to speech distortion due to the mismatch between the separation module and the conversion one. In this paper, a noise-robust voice conversion model is proposed, where a user can choose to retain or to remove the background sounds freely. Firstly, a speech separation module with a dual-decoder structure is proposed, where two decoders decode the denoised speech and the background sounds, respectively. A bridge module is used to capture the interactions between the denoised speech and the background sounds in parallel layers through information exchanging. Subsequently, a voice conversion module with multiple encoders to convert the estimated clean speech from the speech separation model. Finally, the speech separation and voice conversion module are jointly trained using a loss function combining cycle loss and mutual information loss, aiming to improve the decoupling efficacy among speech contents, pitch, and speaker identity. Experimental results show that the proposed model obtains significant improvements in both subjective and objective evaluation metrics compared with the existing baselines. The speech naturalness and speaker similarity of the converted speech are 3.47 and 3.43, respectively. [ABSTRACT FROM AUTHOR]

Ship-radiated noise is the main basis for ship detection in underwater acoustic environments. Due to the increasing human activity in the ocean, the captured ship noise is usually mixed with or covered by other signals or noise. On the other hand, due to the softening effect of bubbles in the water generated by ships, ship noise undergoes non-negligible nonlinear distortion. To mitigate the nonlinear distortion and separate the target ship noise, blind source separation (BSS) becomes a promising solution. However, underwater acoustic nonlinear models are seldom used in research for nonlinear BSS. This paper is based on the hypothesis that the recovery and separation accuracy can be improved by considering this nonlinear effect in the underwater environment. The purpose of this research is to explore and discover a method with the above advantages. In this paper, a model is used in underwater BSS to describe the nonlinear impact of the softening effect of bubbles on ship noise. To separate the target ship-radiated noise from the nonlinear mixtures, an end-to-end network combining an attention mechanism and bidirectional long short-term memory (Bi-LSTM) recurrent neural network is proposed. Ship noise from the database ShipsEar and line spectrum signals are used in the simulation. The simulation results show that, compared with several recent neural networks used for linear and nonlinear BSS, the proposed scheme has an advantage in terms of the mean square error, correlation coefficient and signal-to-distortion ratio. [ABSTRACT FROM AUTHOR]

Purpose: The mechanical fault diagnosis method based on deep learning mainly uses single-scale convolution kernels to extract fault features, which is difficult to extract fault feature information comprehensively. Under strong noise conditions, the performance of fault diagnosis using single-scale convolution kernels will decrease sharply. Methods: An intelligent fault diagnosis method based on multi-scale deep convolution neural network (MSD-CNN) model and data enhancement for strong noise is proposed in this paper. By the multi-scale cascade convolution kernels in the MSD-CNN, the multi-scale information of the original fault signal is extracted, and the ELU activation function is used to retain the negative information contained in the multi-scale information. By the data enhancement method for strong noise, the number and diversity of the MSD-CNN model training samples are improved, which enables the model to learn deeper features in the training stage. Results and Conclusion: Compared six common fault diagnosis models, the proposed model achieves the optimal diagnostic accuracy of 99.98% and 99.66% under normal conditions and strong noise conditions, respectively, which verifies the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

We establish a large deviation principle (LDP) for a class of stochastic porous media equations driven by Lévy-type noise on a σ-finite measure space (E,B(E),μ), with the Laplacian replaced by a negative definite self-adjoint operator. One of the main contributions of this paper is that we do not assume the compactness of embeddings in the corresponding Gelfand triple, and to compensate for this generalization, a new procedure is provided. This is the first paper to deal with LDPs for stochastic evolution equations with Lévy noise without compactness conditions. The coefficient Ψ is assumed to satisfy nondecreasing Lipschitz nonlinearity, so an important physical problem covered by this case is the Stefan problem. Numerous examples of negative definite self-adjoint operators are applicable to our results, for example, for open E⊂Rd, L= Laplacian or fractional Laplacians, i.e., L=-(-Δ)α, α∈(0,1], generalized Schrödinger operators, i.e., L=Δ+2∇ρ/ρ⋅∇, Laplacians on fractals is also included. [ABSTRACT FROM AUTHOR]

In this paper, we focus on a general linear time-invariant multiagent system with heterogeneous actuator saturation to answer the main question: how to achieve global consensus under the premise of asymptotically null controllable with bounded controls of each agent so as to make breakthroughs from the perspectives of global consensus, heterogeneous actuator saturation, communication noises, and distributed characteristic preservation. We introduce a distributed control algorithm that incorporates a redesigned saturation function featuring a decentralized dynamic saturation level to achieve these objectives. Each component of the dynamic saturation level self-updates according to an adaptive strategy. The proposed method effectively eliminates heterogeneous actuator saturation by carefully selecting both the constant and time-varying saturation parameters in the adaptive strategy of the dynamic saturation level. Our approach achieves both state feedback--based and output feedback--based global consensus, with the latter utilizing a special coordinate decomposition. Numerical simulations demonstrate the efficacy of our method. [ABSTRACT FROM AUTHOR]

This paper studied the numerical approximation of the stochastic differential equations driven by non-global Lipschitz drift coefficient and multiplicative noise. An efficient data-driven method, called extended continuous latent process flow, was proposed for the underlying problem. Compared with the piecewise construction of a variational posterior process used in the classical continuous latent process flow developed by Deng et al. [13], the principle idea of our method was to derive a variational lower bound by constructing a posterior latent process conditional on all information over the whole time interval to maximize the log-likelihood generated by the observations, which reduces the computational cost and, thus, provides a convenient way to approximate the considered equation. Particularly, our new method showed a better approximation to the underlying equation than the classical drift-θ discretization scheme through numerical error comparison. Numerical experiments were finally reported to demonstrate the effectiveness and generalization performance of the proposed method. [ABSTRACT FROM AUTHOR]

Despite general agreement that rail corrugation generates unwanted noise, there is a need to quantify the relationship between rail corrugation and noise and to leverage this relationship within preventive rail grinding programs. This paper develops a novel rail corrugation index (RCI) and demonstrates the suitability of that index to characterize the relationship between corrugation and noise, to assess grind effectiveness, and to predict noise as a function of rail corrugation. Using a time-series data set collected at a North American rail transit property, the proposed RCI illustrated corrugation growth as a function of accumulated tonnage and an expected reduction of corrugation after grinding. The RCI also correlated well with corresponding wayside noise observations. The evident response behavior and the relationship between rail corrugation and wayside noise gave rise to the assessment of grind effectiveness using the RCI. Further, a modelling effort demonstrated that noise can be predicted using the RCI calculated from only one rail (left or right) on a tangent section. This result suggests that there may also be an opportunity to predict corrugation using noise data, thus limiting the need for track downtime required to measure corrugation. [ABSTRACT FROM AUTHOR]

PWM (pulse width modulation) is the most widely applied current conversion technology, but the high-frequency harmonics it causes have a significant negative impact on inverter system performance. This paper focuses on the three-phase T-type three-level inverter as the research object and addresses existing PWM voltage noise and midpoint potential imbalance issues by proposing an improved random SVPWM strategy, named Neutral Point Potential Balance Random Space Vector PWM (NPB–RSVPWM). The NPB–RSVPWM strategy includes three main steps: (1) introducing a midpoint potential balancing control loop to adjust the synthesis timing of the effective vectors to generate pulse signals, optimizing midpoint potential balance; (2) employing a randomly varying carrier frequency in place of the carrier used in the SVPWM strategy to generate the driving signals for switching devices; and (3) controlling the inverter through the driving pulse signals. This strategy optimizes the synthesis sequence of traditional SVPWM strategy vectors and incorporates random frequency modulation techniques. The mathematical model analyzes PWM harmonic expressions corresponding to fixed switching frequencies, and a random frequency carrier is chosen to suppress these PWM harmonics. The effective vector's equivalent circuit is analyzed, proposing a technique for optimized vector synthesis timing. The simulation and experimental results verify that the NPB–RSVPWM technique can disperse PWM harmonic energy, reduce voltage noise, and optimize midpoint potential balance. Under the NPB–RSVPWM strategy, the line voltage spectrum becomes uniform, the maximum harmonic content is greatly reduced, and the fluctuation in the DC side midpoint potential is significantly improved. Compared with the traditional SVPWM strategy and random PWM strategy, the NPB–RSVPWM strategy has a lower voltage noise, smaller total harmonic distortion, and a more stable midpoint potential. The effectiveness and feasibility of the NPB–RSVPWM strategy are verified by simulation and experimental results. [ABSTRACT FROM AUTHOR]

The holographic signal-processing method for a single vector scalar receiver (VSR) in the high-frequency band in shallow water is developed in the paper. The aim of this paper is to present the results of the theoretical analysis, numerical modeling, and experimental verification of holographic signal processing for a noise source by the VSR. The developed method is based on the formation of the 2D interferogram and 2D hologram of a noise source in a shallow-water waveguide. The 2D interferograms and 2D holograms for different channels of the VSR (P sound pressure and V X and V Y vibration velocity components) are considered. It is shown that the 2D interferogram consists of parallel interference fingers in the presence of a moving noise source. As a result, the 2D hologram contains focal points located on a straight line, and the angular distribution of the holograms has the main extreme value. It is shown in the paper that the holographic signal-processing method allows detecting the source, estimating the source bearing, and filtering the useful signal from the noise. The results of the source detection, source bearing estimation, and noise filtering are presented within the framework of experimental data processing and numerical modeling. [ABSTRACT FROM AUTHOR]

The template matching method has been widely utilized in the defect detection of wafer surfaces. However, the traditional matching approaches are limited by illumination, noise, and deformation, which cannot meet the requirements of accuracy and robustness. In this paper, a novel multiple targets localization method, named Normalized Cross-correlation Adaptive Variable Step-Size Dynamic Template (NCC-AVSSDT) matching, is proposed to improve the accuracy and efficiency of image localization, which combines the advantages of NCC and AVSSDT. The AVSSDT method is utilized to dynamically adjust the scanning step size based on the NCC matching coefficients. This approach optimizes the scanning process, accelerating convergence toward the optimal matching position. Experimental results verify the accuracy and robustness of the proposed method under different conditions, especially when dealing with rotational variations and variations in noise textures. Therefore, NCC-AVSSDT can be used to perform multiple targets localization of chip image in nearly real-time. Three experiment types were used for comprehensive evaluations, including multiple targets, noise, and rotation angles. Experimental results show that NCC-AVSSDT is much better than the sequential similarity detection algorithm and mean absolute deviation methods in terms of multiple targets (0.667 vs 0.811 s, 0.832 s) and success rate (100% vs 35%, 20%). [ABSTRACT FROM AUTHOR]

Professionals working in both the physical and cybersecurity domain need to assess and evaluate security risks. As information on risks in general and security risks in particular is often imperfect and intractable, these professionals are facing a challenge in judging both likelihood and consequences, but how much do their existing psychological biases play a role in these judgments? In this paper, we present new empirical evidence on the perception of the information position and confidence levels of security professionals, the influence of detailed information and the conjunction fallacy, and the level of noise in security assessments. This paper adds to the literature by examining, for the first time, risk assessments by professionals in realistic, real life, security cases. The results show clear indications for overconfidence, comparative ignorance, influence of the conjunction fallacy, and influence of individual experience on security decision making in the professional security domain. The observed phenomena might have far reaching effects on security risk management in organizations and society. [ABSTRACT FROM AUTHOR]

The intrinsic noise of different transformers in the same substation belongs to the same type of noise, which is strongly coherent and difficult to separate, greatly increasing the cost of substation noise assessment and treatment. To solve the problem, the present paper proposes a noise separation technique using the phase conjugation method to separate the intrinsic noise signals of different transformers: firstly, the reconstruction of sound source information is realized by the phase conjugation method based on the measurement and emission of a line array; secondly, the intrinsic noise signals of the sound source are obtained by the equivalent point source method. The error of the separation technique is analyzed by point source simulation, and the optimal arrangement form of the microphone line array is studied. A validation experiment in a semi-anechoic chamber is also carried out, and the results prove that the error of separation technique is less than 2dBA, which is the error tolerance of engineering applications. Finally, a noise separation test of three transformers is performed in a substation using the proposed technique. The results show that the proposed technique is able to realize the intrinsic noise separation of each transformer in the substation, which is of positive significance for substation noise assessment and management. [ABSTRACT FROM AUTHOR]

This study presents a predominantly numerical and theoretical investigation into the balance of tonal and broadband noise due to an isolated propeller in uniform motion. The predicted trends in the balance between tonal and broadband noise radiation with varying blade number and speed of rotation is supported by preliminary experimental measurements. Here, we assume that the dominant noise generation mechanisms are the tones due to steady loading and blade thickness, while the broadband noise is due to boundary layer scattering at the trailing edge. The study also provides a detailed comparison between the tonal and broadband formulations to highlight their similarities and differences. In this paper, we show that the main differences in the behaviour and character of the tonal and broadband spectra and directivities are due to the number of acoustic modes that can be excited. This paper presents a parametric study in which the variation in tonal and broadband noise is investigated as a function of blade tip Mach number (M t ) and blade number (B) whilst maintaining constant solidity and thrust. This study is repeated for three NACA airfoil profiles. It is found that tonal noise dominates at low blade number and low frequency and/or higher tip speeds, while broadband noise is the major contributor at high-frequencies and at high blade number and low tip speeds. The results show a clear distinction between the combinations of M t and B that are dominated by tonal and by broadband noise. These results are interpreted from fundamental principles relating to modal radiation efficiencies. We confirm this trend of balance between tonal and broadband noise with measured noise at different B and M t . The results of this paper will serve as useful guidelines for preliminary propeller design. [ABSTRACT FROM AUTHOR]

Definition: This entry paper is an attempt to explain how the discrete nature of light (energy discreteness in the form of photons) constrains the light detection process all along the evolutionary path, in the not-fully-understood photoreceptive systems of unicellular microorganisms (nonimaging systems) and in the complex and well-known visual system of higher organisms (imaging systems). All these systems are perfect examples of the interplay between physics and biology, i.e., they are the perfect topic of research for biophysicists. The paper describes how photoreceptive and visual systems achieve the goal of photon counting, which information is conveyed by a finite number of photons, and which noise factors limit light-detecting processes. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to highlight the problem of the impact of vibration and noise recorded on selected new-generation DP-class shuttle tankers operated in the Arctic offshore sector. The paper presents the functional and disease effects associated with excessive exposure to these physical factors, the levels of which exceed the normatively acceptable values. The work also discusses the impact of physical factors on the marine environment. The international community recognizes that noise and vibrations from commercial ships may have very negative consequences for both humans (worker's) and marine life, especially marine mammals. However, there are also certain legal requirements in maritime transport that require adaptation to noise and vibration control when working on ships. The acceptable noise and vibration exposure standards set out in European Union Directive 2003/10/EC (2003), the NOPSEMA Regulation (2006), the Maritime Labour Convention (MLC) guidelines (2006) and the recommendations of the International Maritime Organization IMO contained, e.g. IMO MEPC.1 / Circ.833 (2014). These regulations inform employers and employees what they must do to effectively protect both the marine environment and the health and life safety of workers employed in the maritime industry offshore. This study also presents an analysis of the results of noise measurements carried out on selected DP class Shuttle Tanker operated in the Arctic sector offshore. The article presents the methods of noise measurement and assessment, but does not discuss personal protective equipment and ship's noise protection systems. [ABSTRACT FROM AUTHOR]

Aviation is a vital modern transportation sector connecting millions of passengers globally. Sustainable aviation development holds substantial community benefits, necessitating effective management of its environmental impacts. This paper addresses the need for an accurate and cost-effective aircraft noise monitoring model tailored to non-towered general aviation airports with limited resources for official air traffic data collection. The existing literature highlights a heavy reliance on air traffic data from control facilities in prevailing aircraft noise modeling solutions, revealing a disparity between real-world constraints and optimal practices. Our study presents a validation of a three-stage framework centered on a low-cost transponder unit, employing an innovative experimental and analytical approach to assess the model's accuracy. An economical Automatic Dependent Surveillance-broadcast (ADS-B) receiver is deployed at Purdue University Airport (ICAO Code: KLAF) to estimate aircraft noise levels using the developed approach. Simultaneously, a physical sound meter is positioned at KLAF to capture actual acoustic noise levels, facilitating a direct comparison with the modeled data. Results demonstrate that the developed noise model accurately identifies aircraft noise events with an average error of 4.50 dBA. This suggests the viability of our low-cost noise monitoring approach as an affordable solution for non-towered general aviation airports. In addition, this paper discusses the limitations and recommendations for future research. [ABSTRACT FROM AUTHOR]

Algorithms for QRS detection are fundamental in the ECG interpretive processing chain. They must meet several challenges, such as high reliability, high temporal accuracy, high immunity to noise, and low computational complexity. Unfortunately, the accuracy expressed by missed or redundant events statistics is often the only parameter used to evaluate the detector's performance. In this paper, we first notice that statistics of true positive detections rely on researchers' arbitrary selection of time tolerance between QRS detector output and the database reference. Next, we propose a multidimensional algorithm evaluation method and present its use on four example QRS detectors. The dimensions are (a) influence of detection temporal tolerance, tested for values between 8.33 and 164 ms; (b) noise immunity, tested with an ECG signal with an added muscular noise pattern and signal-to-noise ratio to the effect of "no added noise", 15, 7, 3 dB; and (c) influence of QRS morphology, tested on the six most frequently represented morphology types in the MIT-BIH Arrhythmia Database. The multidimensional evaluation, as proposed in this paper, allows an in-depth comparison of QRS detection algorithms removing the limitations of existing one-dimensional methods. The method enables the assessment of the QRS detection algorithms according to the medical device application area and corresponding requirements of temporal accuracy, immunity to noise, and QRS morphology types. The analysis shows also that, for some algorithms, adding muscular noise to the ECG signal improves algorithm accuracy results. [ABSTRACT FROM AUTHOR]

The present paper is aimed at providing an updated review of prediction methods for the aerodynamic noise of ducted rotor–stator stages. Indeed, ducted rotating-blade technologies are in continuous evolution and are increasingly used for aeronautical propulsion units, power generation and air conditioning systems. Different needs are faced from the early design stage to the final definition of a machine. Fast-running, approximate analytical approaches and high-fidelity numerical simulations are considered the best-suited tools for each, respectively. Recent advances are discussed, with emphasis on their pros and cons. [ABSTRACT FROM AUTHOR]

Passive acoustic data collected during 2020 and 2021 were used to monitor changes in both terrestrial and underwater soundscapes, as well as human activity from aircraft and vessels. Passive acoustic data were collected at two artificial reefs south of Long Island, as well as along ocean beaches in Southampton, NY. At the artificial reefs, vessel noise was recorded more frequently during 2020 than in 2021. Commercial vessels and multi-user charter fishing vessels were more abundant during 2020. Peaks in power spectral density occurred at 60, 90 and 120 Hz in 2020 and 2021, which are frequencies consistent with noise generated by commercial vessels, suggesting that vessels are a significant contributor to the soundscape of the artificial reefs. In the terrestrial environment, noise generated by aircraft was more common during 2021. Peaks in power spectral density were measured around 160 and 290 Hz at one of the ocean beach sites. These frequencies are consistent with noise generated by aircraft. This study documents the chronic extent of anthropogenic noise in both the underwater and terrestrial environments of Long Island, NY, as well as quantifies the occurrence of various noise sources in these habitats., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Anthropogenic noise is considered one important global pollutant. The impact of noise on marine invertebrates has been less assessed. The present study evaluated the chronic effect of the motorboat noise obtained from a lagoon's soundscape, the natural habitat of the key crab Neohelice granulata, on its whole embryonic development, considering morphological and physiological carryover effects on embryos and hatched larvae. Results demonstrated that embryonic development was shortened under noise exposure. The effects on advanced embryos, larvae and adult females were: increased heartbeats and non-viable eggs, and decreased fecundity. Biochemical responses showed lipid peroxidation in embryos while antioxidant enzymes were activated in larvae and adults, indicating a counteracting effect related to the life stage. The negative effects on fitness offspring may imply ecological consequences at the population level. Results are discussed in terms of the ecosystem engineer species studied and the habitat, a MAB UNESCO Reserve lagoon, suggesting the urgent need to develop mitigation plans., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Anthropogenic noise is a global pollutant but its potential impacts on early life-stages in fishes are largely unknown. Here, using controlled laboratory experiments, we tested for impacts of continuous or intermittent exposure to low-frequency broadband noise on early life-stages of the common goby (Pomatoschistus microps), a marine fish with exclusive paternal care. Neither continuous nor intermittent noise exposure had an effect on filial cannibalism, showing that males were capable and willing to care for their broods. However, broods reared in continuous noise covered a smaller area and contained fewer eggs than control broods. Moreover, although developmental rate was the same in all treatments, larvae reared by males in continuous noise had, on average, a smaller yolk sac at hatching than those reared in the intermittent noise and control treatments, while larvae body length did not differ. Thus, it appears that the increased consumption of the yolk sac reserve was not utilised for increased growth. This suggests that exposure to noise in early life-stages affects fitness-related traits of surviving offspring, given the crucial importance of the yolk sac reserve during the early life of pelagic larvae. More broadly, our findings highlight the wide-ranging impacts of anthropogenic noise on aquatic wildlife living in an increasingly noisy world., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Speech enhancement is crucial both for human and machine listening applications. Over the last decade, the use of deep learning for speech enhancement has resulted in tremendous improvement over the classical signal processing and machine learning methods. However, training a deep neural network is not only time-consuming; it also requires extensive computational resources and a large training dataset. Transfer learning, i.e. using a pretrained network for a new task, comes to the rescue by reducing the amount of training time, computational resources, and the required dataset, but the network still needs to be fine-tuned for the new task. This paper presents a novel method of speech denoising and dereverberation (SD&D) on an end-to-end frozen binaural anechoic speech separation network. The frozen network requires neither any architectural change nor any fine-tuning for the new task, as is usually required for transfer learning. The interaural cues of a source placed inside noisy and echoic surroundings are given as input to this pretrained network to extract the target speech from noise and reverberation. Although the pretrained model used in this paper has never seen noisy reverberant conditions during its training, it performs satisfactorily for zero-shot testing (ZST) under these conditions. It is because the pretrained model used here has been trained on the direct-path interaural cues of an active source and so it can recognize them even in the presence of echoes and noise. ZST on the same dataset on which the pretrained network was trained (homo-corpus) for the unseen class of interference, has shown considerable improvement over the weighted prediction error (WPE) algorithm in terms of four objective speech quality and intelligibility metrics. Also, the proposed model offers similar performance provided by a deep learning SD&D algorithm for this dataset under varying conditions of noise and reverberations. Similarly, ZST on a different dataset has provided an improvement in intelligibility and almost equivalent quality as provided by the WPE algorithm., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Gul et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Passive acoustics is an effective method for monitoring marine mammals, facilitating both detection and population estimation. In warm tropical waters, this technique encounters challenges due to the high persistent level of ambient impulsive noise originating from the snapping shrimp present throughout this region. This study presents the development and application of a neural-network based detector for marine-mammal vocalizations in long term acoustic data recorded by us at ten locations in Singapore waters. The detector's performance is observed to be impeded by the high shrimp noise activity. To counteract this, we investigate several techniques to improve detection capabilities in shrimp noise including the use of simple nonlinear denoisers and a machine-learning based denoiser. These are shown to enhance the detection performance significantly. Finally, we discuss some of the vocalizations detected over three years of our acoustic recorder deployments using the robust detectors developed., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)