Noise Estimation Method Research Articles

Advanced concept for noise monitoring in smart cities through wireless sensor units with AI classification technologies

The rapid urbanization in the cities has indicated the importance of sustainable development for creating smart cities with high quality of life. Environmental noise as one of the main concerns has to be addressed according to International Directives and Legislation. Traditional noise estimation methods are costly and time-consuming, but the emerging technologies like low-cost wireless sensor units (WSUs) offer more granular data collection and analysis. Requirements for more accurate assessment of the noise pollution by detecting the dominant noise sources impose the need to apply novel approaches in the technical systems for noise monitoring. To this aim, this paper investigates the advanced methods for noise monitoring through WSUs with AI classification technologies. The proposed concept of the device not only quantitively describes the noise pollution, but also tends to recognize the class of the disturbing sound events. The accuracy of the WSUs are configured by comparing the results with professional hand-held analyzer, showing powerful and affordable low-cost units able to publish results through cloud and fog computing based on the IoT and smart city technologies. The paper discusses the challenges for merging noise measurement technology and the AI algorithms to classify and quantify the different classes of sound events.

Adaptive dual-domain filtering for random seismic noise removal

Random noise significantly reduces the signal-to-noise ratio of seismic data and affects the accuracy of seismic interpretation. Traditional denoising methods typically require manual parameter tuning to increase robustness and accuracy across various random noise levels. In this study, based on the statistical definition of random noise, we use the variance of random noise as the level of random noise and develop an adaptive dual-domain filter (ADDF). The ADDF method estimates the random noise variance in the seismic data and uses this estimation to effectively denoise the seismic data. First, we use a difference operator in two directions to remove the useful structures from the seismic data. The processed data are then used to estimate the global random noise variance through iterative statistical processing. In the denoising process of the ADDF, the seismic data are masked by a bilateral filter in the spatial domain, followed by a short-time Fourier transform with wavelet shrinkage in the frequency domain, both controlled by the adaptively estimated random noise variance. The dual-domain filter is applied iteratively for the best performance. Synthetic experiments demonstrate the robustness of the ADDF in accurately estimating the noise variance without tuning parameters, and its superior denoising performance is evident in synthetic examples and field data when compared with two typical denoising methods: f- x deconvolution and curvelet domain thresholding. As an adaptive random noise estimation and removal method, ADDF relies only on seismic data, making denoising random noise more objective and accurate without manual adjustment.

Learning real-world heterogeneous noise models with a benchmark dataset

Noise modeling is an important research field in computer vision; the design of an accurate model for imaging sensor noise depends on not only a comprehensive benchmark dataset of the real world, but also a precise design of the noise modeling algorithm. However, due to the inaccurate estimation method of noise-free images and limited shooting scenes, the current realistic datasets could not describe the diverse noise properties sufficiently. Moreover, popular parametric noise models are not sophisticated enough to characterize the real-world noise exactly. In this work, we first construct a more comprehensive dataset of the real world by capturing more indoor and outdoor scenes under different lighting conditions using diverse smartphones, then we propose a non-parametric noise estimation method capable of modeling the spatial heterogeneity of real-world noise patterns. Specifically, in order to characterize the spatial heterogeneity of real-world noise, we assume a non-i.i.d Gaussian distribution and propose a deep convolutional neural network (DCNN)-based approach for learning pixel-wise noise variance maps. To learn the pixel-wise variance map, we have constructed a variance estimation network mapping from the conditional signals (clean image, ISO, and camera model) to surrogate labels obtained from the nonlocal search of similar patches from the clean-noisy image pair. Finally, we conducted denoising and classification experiments using different kinds of simulated noisy images, compared to the Poisson-Gaussian and Noise Flow noise models, the proposed method achieves denoising performance improvements (PSNR) of 1.13 dB and 2.51 dB respectively on the proposed real-world test dataset, denoising and classification results on the real noisy data captured by mobile phones have verified that our approach is more accurate than current noise modeling methods.

Detailed CNEL/Ldn roadway noise calculations vs. estimation methods

Roadway traffic noise calculations of the community noise equivalent level (CNEL) and the day-night average sound level (Ldn) metrics are sometimes required for various federal, state, and local government permitting requirements. To complete calculations of these metrics, detailed hourly traffic data is needed and obtaining this data can sometimes be a challenge. This paper explores various estimation strategies that are sometimes employed for situations where this data is not available and compares the estimates against calculations using detailed hourly traffic data to assess their validity. Project examples are provided of various permitting efforts such as those in support of California Environmental Quality Act (CEQA) documents, roadway projects at airports for Federal Aviation Administration (FAA) National Environmental Policy Act (NEPA) documents, and other local permitting requirements.

Automated construction of effective potential via algorithmic implicit bias

We introduce a novel approach for decomposing and learning every scale of a given multiscale objective function in Rd, where d⩾1. This approach leverages a recently demonstrated implicit bias of the optimization method of gradient descent [44], which enables the automatic generation of data that nearly follow Gibbs distribution with an effective potential at any desired scale. One application of this automated effective potential modeling is to construct reduced-order models. For instance, a deterministic surrogate Hamiltonian model can be developed to substantially soften the stiffness that bottlenecks the simulation, while maintaining the accuracy of phase portraits at the scale of interest. Similarly, a stochastic surrogate model can be constructed at a desired scale, such that both its equilibrium and out-of-equilibrium behaviors (characterized by auto-correlation function and mean path) align with those of a damped mechanical system with the original multiscale function being its potential. The robustness and efficiency of our proposed approach in multi-dimensional scenarios have been demonstrated through a series of numerical experiments. A by-product of our development is a method for anisotropic noise estimation and calibration. More precisely, Langevin model of stochastic mechanical systems may not have isotropic noise in practice, and we provide a systematic algorithm to quantify its covariance matrix without directly measuring the noise. In this case, the system may not admit closed form expression of its invariant distribution either, but with this tool, we can design friction matrix appropriately to calibrate the system so that its invariant distribution has a closed form expression of Gibbs.

Phase Noise Effects on OFDM Chirp Communication Systems: Characteristics and Compensation

Orthogonal frequency-division multiplexing (OFDM) chirp waveforms are an attractive candidate to be a dual-function signal scheme for the joint radar and communication systems. OFDM chirp signals can not only be employed to transmit communication data through classic phase modulation, but also can perform radar detection by applying linear frequency modulation for subcarriers. However, the performance of the OFDM chirp communication system under the phase noise environment still remains uninvestigated. This paper tries to discuss the influence of phase noise on OFDM chirp communication systems and proposes effective phase noise estimation and compensation methods. We find that the phase noise effect on OFDM chirp communication systems consists of a common phase error (CPE) and an inter-carrier interference (ICI). If not compensated, the performance of the dual-function systems can be seriously degraded. In particular, an exact expression for the signal-plus-interference to noise ratio (SINR) for the OFDM chirp communication system is derived and some critical parameters are analyzed to exhibit the phase noise effects on system performance. Moreover, two low-complexity estimation approaches, maximum likelihood (ML) and linear minimum mean square error (LMMSE), as well as two compensation approaches, the de-correlation and cancellation algorithms, are respectively utilized to eliminate the phase noise impairment. Finally, the phase noise effects and the effectiveness of the compensation approach are verified by extensive numerical results.

Monocular 3D Hand Mesh Recovery via Dual Noise Estimation

Current parametric models have made notable progress in 3D hand pose and shape estimation. However, due to the fixed hand topology and complex hand poses, current models are hard to generate meshes that are aligned with the image well. To tackle this issue, we introduce a dual noise estimation method in this paper. Given a single-view image as input, we first adopt a baseline parametric regressor to obtain the coarse hand meshes. We assume the mesh vertices and their image-plane projections are noisy, and can be associated in a unified probabilistic model. We then learn the distributions of noise to refine mesh vertices and their projections. The refined vertices are further utilized to refine camera parameters in a closed-form manner. Consequently, our method obtains well-aligned and high-quality 3D hand meshes. Extensive experiments on the large-scale Interhand2.6M dataset demonstrate that the proposed method not only improves the performance of its baseline by more than 10% but also achieves state-of-the-art performance. Project page: https://github.com/hanhuili/DNE4Hand.

Wind Noise Estimation Method in Low-Frequency Sound Measurement in Windy Outdoor Environment

It is generally known that low-frequency sound is affected by natural winds when measured on-site. In this paper, the estimation method of wind noise by wind tunnel and outdoor natural wind has been examined step by step. Firstly, wind tunnel experiments were conducted to confirm the relationship between wind velocity time average values and turbulence intensity, and to create the estimation fornula. Secondary, the field experiment was conducted using a low-frequency sound generator as a sound source, and the different results between the wind tunnel experiment and outdoor experiment were discovered. Then, we accumulated the field data in different roughness divisions and tried to create the wind noise estimation fornula at each point. As a result, we considered the different results according to the roughness division and obtained the results that it is possible to estimate the wind noise by using the proposed estimation fornula.

Single image noise level estimation by artificial noise

In this paper, we propose a new method for noise level estimation from a single noisy image contaminated by additive white Gaussian noise and gamma noise, which usually appear due to different physical imaging systems. Our method is motivated by the key observation that the curves of small eigenvalues of the patch covariance matrices corresponding to the original noisy image and the artificial noisy image are nearly parallel. Based on some assumptions, we derive a proportional relation equation between eigenvalues and variances of the noisy images in the redundant dimensions. The solution of this equation gives the basic noise estimation formula. However, the assumptions are not strictly satisfied for natural images with textures and fine details. To overcome this drawback, we modified the basic noise estimation formula by introducing a correction parameter. The proposed method is also extended to multiplicative Gamma noise estimation. Numerical results demonstrate that the proposed noise level estimation method outperforms the existing state-of-the-art approaches in accuracy for various scenes. In particular, the decreasing of Root Mean Square Error (RMSE) is an average of over 10% for the testing databases. We further demonstrate that the proposed noise estimation method can be used for blind denoising.

A Unified Differential Mode Noise Estimation Method and Filter Size Comparison in Single-Phase Multileg and Multilevel Totem-Pole PFC Converters

Wide band-gap power devices have enabled single-phase totem-pole (TP) power factor correction (PFC) converter designs to be switched at several tens of kHz under hard-switching and hundreds of kHz under soft-switching. It is straight-forward to analyze and compare the volume of boost inductor for TP PFC converters having multi-leg and multi-level configurations operating under continuous-conduction-mode (CCM) and critical-conduction-mode (CrM) at various switching frequencies; however, the impact of topology and switching frequency selections remains unchallenged for generated differential mode (DM) noise. This paper proposes a unified and generalized DM noise estimation method for multi-leg, and multi-level TP PFC converters operated under both CCM and CrM operation modes. The method has been verified with simulation results obtained from various TP PFC topologies, as well as experimental results obtained from single-leg CCM and 4-Level CCM TP PFC converters. The empirically determined boxed sizes of DM filters have been compared among various TP PFC topologies, and the best switching frequency for each topology has been determined with the integration of boost inductor volume comparison.

A double sliding-window method for baseline correction and noise estimation for Raman spectra of microplastics

When measuring microplastics of environmental samples, additives and attachment of biological materials may result in strong fluorescence in Raman spectra, which increases difficulty for imaging, identification, and quantification. Although there are several baseline correction methods available, user intervention is usually needed, which is not feasible for automated processes. In current study, a double sliding-window (DSW) method was proposed to estimate the baseline and standard deviation of noise. Simulated spectra and experimental spectra were used to evaluate the performance in comparison with two popular and widely used methods. Validation with simulated spectra and spectra of environmental samples showed that DSW method can accurately estimate the standard deviation of spectral noise. DSW method also showed better performance than compared methods when handling spectra of low signal-to-noise ratio (SNR) and elevated baselines. Therefore, DSW method is a useful approach for preprocessing Raman spectra of environmental samples and automated processes.

Influence of adaptive denoising on Diffusion Kurtosis Imaging at 3T and 7T

Background and objectiveChoosing the most appropriate denoising method to improve the quality of diagnostic images maximally is key in pre-processing of diffusion MRI images. Recent advancements in acquisition and reconstruction techniques have questioned traditional noise estimation methods favoring adaptive denoising frameworks, circumventing the need to know a priori information that is hardly available in a clinical setting. In this observational study, we compared two innovative adaptive techniques sharing some features, Patch2Self and Nlsam, through application on reference adult data at 3T and 7T. The primary aim was identifying the most effective method in case of Diffusion Kurtosis Imaging (DKI) data - particularly susceptible to noise and signal fluctuations - at 3T and 7T fields. A side goal consisted of investigating the dependence of kurtosis metrics’ variability with respect to the magnetic field on the adopted denoising methodology. MethodsFor comparison purposes, we focused on qualitative and quantitative analysis of DKI data and related microstructural maps before and after applying the two denoising approaches. Specifically, we assessed computational efficiency, preservation of anatomical details via perceptual metrics, consistency of microstructure model fitting, alleviation of degeneracies in model estimation, and joint variability with varying field strength and denoising method. ResultsAccounting for all these factors, Patch2Self framework has turned out to be specifically suitable for DKI data, with improving performance at 7T. Nlsam method is more robust in alleviating degenerate black voxels while introducing some blurring, which in turn is reflected in an overall loss of image sharpness. Regarding the impact of denoising on field-dependent variability, both methods have been shown to make variations from standard to Ultra-High Field more concordant with theoretical evidence, claiming that kurtosis metrics are sensitive to susceptibility-induced background gradients, directly proportional to the magnetic field strength and sensitive to the microscopic distribution of iron and myelin. ConclusionsThis study serves as a proof-of-concept stressing the need for an accurate choice of a denoising methodology, specifically tailored for the data under analysis and allowing higher spatial resolution acquisition within clinically compatible timings, with all the potential benefits that improving suboptimal quality of diagnostic images entails.

A binaural heterophasic adaptive beamformer and its deep learning assisted implementation

Beamforming is one of the most effective approaches to distant sound acquisition in complex acoustic environments, where noise, reverberation, and interference coexist; as a result, a significant number of efforts have been devoted to it over the last few decades. However, conventional beamformers produce a monaural output or colinear outputs, which are not optimal from the perception perspective. To take advantage of the human binaural hearing properties, a new type of fixed beamforming methods were developed recently, which attempt not only to attenuate noise but also render the signal of interest and residual noise into different perceptual regions, thereby achieving higher speech intelligibility. This work extends the principle of fixed binaural beamforming and develops a binaural heterophasic minimum variance distortionless response (MVDR) beamformer. A deep neural network (DNN) based noise estimation method is used to assist the implementation of this heterophasic MVDR beamformer, which is advantageous over the traditional one as it renders the desired source signal and residual noise to different perceptual regions, thereby yielding higher intelligibility. In comparison with the fixed binaural heterophasic beamformers, it can take advantage of the statistics of the noise to achieve better array performance. Results of simulations and listening tests validate the properties of the proposed technique.

Estimation method for vehicle interior noise and vibration contribution estimation by Operational and Component TPA - Basic consideration employing simple vehicle model -

Estimation method for vehicle interior noise and vibration contribution estimation by Operational and Component TPA - Basic consideration employing simple vehicle model -

A Digital Self-Interference Cancellation Scheme for In-Band Full-Duplex-Applied 5G System and Its Software-Defined Radio Implementation

The development of mobile communication technology toward 6G is difficult because the required spectrum resources have already been allocated to existing systems. Therefore, new communication systems must have high spectral efficiency to effectively utilize the available resources. This article proposes a digital self-interference (SI) canceller that can be applied to full-duplex cellular systems based on the current 5G signal format (5G-FDC) to introduce in-band full-duplex systems beyond 5G. Specifically, we reorganize the 5G demodulation reference signal (DMRS) and define a novel DMRS configuration, in which the DMRSs for uplink and downlink communications do not interfere with each other. The proposed DMRS configuration effectively improves the accuracy of the SI channel estimation. Moreover, we propose a channel extrapolation scheme that suppresses the channel estimation degradation, which deteriorates the block error rate performance. Additionally, we propose a noise estimation method to improve the low-density parity check decoding performance of the desired signal in 5G-FDC systems. The effectiveness of the proposed method is demonstrated through computer simulations and experimentally by using our developed and implemented software-defined radio-based 5G-FDC prototype. Digital SI cancellation performances of 50 dB and 30.6 dB are observed in the simulation and experimental evaluations, respectively.

Beyond a 107 range-resolution-1 product in an OFDR based on a periodic phase noise estimation method.

We present and demonstrate a method based on a periodic phase noise estimation in an optical frequency domain reflectometry (OFDR) capable of a beyond 107 range-resolution-1 product (RRP) for the first time, which corresponds to 2.5 × improvement compared with the state-of-the-art. The moving average filter is employed to suppress the amplification of noise in the derivation process. Further, with the help of a third-order Taylor expansion, this method provides a highly precise estimation of periodic phase noise, which is the main factor impacting the performance of OFDR systems with medium-to-long measurement range combined with a submillimeter spatial resolution. A spatial resolution within 535 μm over the measurement range of 8 km is obtained. The proposed method offers a promising technique for fiber network monitoring and sensing applications.

Estimating sonic boom metrics across a community using a Kalman filter

As part of the Quesst mission, NASA will fly the X-59 aircraft over selected communities to evaluate community responses to low-intensity sonic booms. The purpose of these community tests is to determine the dose-response relationship between the noise exposure metrics and the community response. The independent variables for the dose-response relationship are the noise exposure metrics experienced by survey respondents within the community. Two sources of noise exposure metrics are available in each community: measurements at sparse locations throughout the community, and calculations from propagation models across the community. Both the measurements and calculations are subject to uncertainty. A Kalman filter is proposed to combine the measured and calculated noise exposure metrics to obtain the best estimate of the true noise exposure metrics across the community. The noise exposure metrics estimated by the Kalman filter have lower uncertainty than either the measured or calculated noise exposure metrics alone. Simulations demonstrate that the Kalman filter produces a more accurate estimate of the true noise exposure metrics than other noise estimation methods.

Design of near field noise reduction system for printing press based on Noise Recognition Technology

The near-field noise reduction system of printing press is designed based on the correlation principle of noise signal generation, the characteristics of noise signal and the algorithm of noise related knowledge. Through the analysis and comparison, two methods of segmented signal-to-noise ratio and waveform are selected as the objective method and subjective method to evaluate the algorithm. Considering the factor of time, aiming at the traditional printing press near-field spectrum subtraction, the traditional noise estimation method is used to estimate the minimum statistics and optimal smoothing noise of printing press near-field noise. According to the characteristics of spectrum subtraction, the noise processing method is realized. Finally, the simulation results show that the near-field noise reduction system based on noise recognition technology has higher effectiveness in the practical application process, and fully meets the research requirements.

Noise-Scaled Euclidean Distance: A Metric for Maximum Likelihood Estimation of the PV Model Parameters

This article revisits the objective function (or metric) used in the extraction of photovoltaic (PV) model parameters. A theoretical investigation shows that the widely used current distance (CD) metric does not yield the maximum likelihood estimates (MLE) of the model parameters when there is noise in both voltage and current samples. It demonstrates that the Euclidean distance (ED) should be used instead, when the voltage and current noise powers are equal. For the general case, a new noise-scaled Euclidean distance (NSED) metric is proposed as a weighted variation of ED, which is shown to fetch the MLE of the parameters at any noise conditions. This metric requires the noise ratio (i.e., ratio of the two noise variances) as an additional input, which can be estimated by a new noise estimation (NE) method introduced in this study. One application of the new metric is to employ NSED regression as a follow-up step to existing parameter extraction methods toward fine-tuning of their outputs. Results on synthetic and experimental data show that the so-called NSED regression “add-on” improves the accuracy of five such methods and validate the merits of the NSED metric.

Research on a Real-Time Estimation Method of Vehicle Sideslip Angle Based on EKF.

In this article, a real-time vehicle sideslip angle state observer is proposed, which is based on the EKF algorithm. Firstly, based on a 2-DOF dynamical model and the tire lateral force model, the vehicle sideslip angle state observer model with a self-adapted truncation procedure is established by combining the EKF and the least squares methods. The calculation of the Jacobi matrix in the time domain is transformed into a calculation in the frequency domain. A self-adapted update noise estimation method and an initial value setting strategy are proposed as well. Finally, a hardware-in-the-loop simulation is carried out by Matlab/Simulink-CarSim-dSPACE, and the real-time reliability of the estimation method is verified and analyzed by RMSE.