Noise Samples Research Articles

A study of the role of data and model uncertainty in active learning

Uncertainty-based active learning strategies have demonstrated significant superiority in small data research of materials domain. This study explores the effects of model uncertainty and data uncertainty separately on the performance of active learning strategies, specifically focusing on the number of iterations required to identify the optimal samples. For model uncertainty, three kinds of acquisition functions are compared, including predicted value strategy (PV), ranking of predicted value strategy (PR) and expected improvement strategy (EI). Among these, the active learning model utilizing PR requires the fewest average iterations (1.75). For data uncertainty, we evaluate the iterations of active learning by Gaussian process models that incorporate the uncertainty of the observations and noise samples that takes account into the uncertainty of the input features respectively. The results indicate that the active learning iterations of the three strategies converge to similar at the optimal weighting when the uncertainty of the observations is considered in the model (EI for 1.75, PV for 1.21 and PR for 1.18). In contrast, incorporating noise samples into the augmented dataset after the original samples would severely deteriorate the efficiency of active learning recommendations. Our findings aim to offer guidance for exploring more favorable acquisition functions and methods for active learning strategies.

Bearing fault diagnosis by sparse frequency spiral spectrum driven NAF-LDM under strong noise and small samples

Abstract The complexity of background noise and the scarcity of real fault samples seriously affect the diagnostic accuracy of the model. To address this, a noise-robust two-dimensional feature map, the sparse frequency spiral spectrum (SFSM), based on sparse representation theory, is proposed. A bridge penalty coefficient is applied to the sparse representation model to accurately select impact components, and the fast iterative shrinkage threshold algorithm is used to solve for sparse representation coefficients. Sparse reconstructed signals are obtained by convolving the impact patterns with these coefficients, leading to a sparse reconstruction algorithm with reduced computational complexity. Furthermore, the novel non-linear activation-free blocks (NAF Blocks) are embedded into the latent diffusion model to augment small samples, significantly improving image generation speed and quality. The integration of the Swin transformer for feature extraction and classification further enhances diagnostic performance. The superiority of this method is validated on the XJTU-SY dataset, a bearing experimental platform dataset, and enterprise engineering dataset. Experimental results demonstrate that the structural and generalization advantages of NAF Blocks are crucial for improving image quality and inference speed. The noise suppression capability of the proposed method, facilitated by the SFSM feature processing technique, is confirmed through ablation and noise robustness tests. Finally, the Swin transformer’s excellent feature extraction and classification capabilities for SFSM are verified. The proposed method achieves diagnostic accuracies of 99.10% and 98.7% on the XJTU-SY and experimental platform datasets, respectively.

Design and Research on Identification of Typical Tea Plant Diseases Using Small Sample Learning

Tea plants are susceptible to diseases during their growth. These diseases seriously affect the yield and quality of tea. The effective prevention and control of diseases requires accurate identification of diseases. With the development of artificial intelligence and computer vision, automatic recognition of plant diseases using image features has become feasible. As the support vector machine (SVM) is suitable for high dimension, high noise, and small sample learning, this paper uses the support vector machine learning method to realize the segmentation of disease spots of diseased tea plants. An improved Conditional Deep Convolutional Generation Adversarial Network with Gradient Penalty (C-DCGAN-GP) was used to expand the segmentation of tea plant spots. Finally, the Visual Geometry Group 16 (VGG16) deep learning classification network was trained by the expanded tea lesion images to realize tea disease recognition.

The design of listening tests for impact noise annoyance ratings

Noise annoyance is characterized by irritation, discomfort, disturbance or distraction. The evaluation of impact noise based on user response provides useful information about subjective auditory perception. These assessments are carried out by participants in listening tests under laboratory conditions, where participants usually rate the annoyance caused by different samples of impact noise. The question arises as to whether it is possible to gain a meaningful insight into the human inner life by evaluating only a single quality such as annoyance. Therefore, other qualities such as loudness, allowance limit or naturalness are also included in the rating procedure. In this paper we present the design of the listening test for impact noise annoyance and the corresponding selection of investigated qualities. It describes why they were selected and how the evaluation was carried out. It also describes how descriptive qualities have been used in other studies. The technical implementation of the listening procedure is presented and correlations between the ratings of the different qualities are discussed.

Ultrasonic characterization of defects in polycrystalline materials based on TFM image reconstruction using denoising diffusion probabilistic models

This paper presents a refined ultrasonic total focusing method (TFM) for accurately characterizing key defect parameters including size, orientation, and location in complex materials. The proposed approach aims to accurately reconstruct the defect shape in images compromised by grain interference. First, a TFM image database was generated with varying defect parameters and grain noise levels by adopting a custom forward modeling process based on superposition of defect and grain scattering data. Second, a new architecture called TFM-DDPM was then proposed which incorporates TFM and the conditional denoising diffusion probabilistic model (DDPM). Third, pixel-level uncertainty can be quantified through repeated sampling of noise which follows a standard normal distribution. An acceptance criterion for the reconstruction was further established by analyzing the quantified uncertainty. In simulation, the median Dice coefficient between the de-noised TFMs of 30° cracks and their corresponding ground truth images increased from 0.5585 to 0.9137 using the proposed approach, and the median IoU metric rose from 0.3875 to 0.8408. By applying the 6-dB drop approach to the reconstructed images, the root mean squared errors (RMSEs) of size, angle and location were decreased by 93.59%, 86.90% and 86.25%, respectively. It is also demonstrated that our method can reject 69.19% of incorrect characterization results caused by high levels of noise and/or images with steeply inclined (e.g., 45°) cracks, while accepting 97.29% accurate results. Experimental results obtained on nickel-based alloy K403 specimens also exhibited significant improvements. For 2–3 mm cracks, the average sizing error reduced from 0.46 mm to 0.13 mm, and the error in crack angle decreased from 28.63° to 1.50°. Comparable performance enhancements were observed for 4–5 mm cracks using the proposed approach.

A [formula omitted]-means triangular synthesis large margin classifier with unified pinball loss for imbalanced data

Large Margin Distribution Machine (LDM) improves the Support Vector Machine (SVM) by integrating the marginal distribution of samples into the objective function, which exhibits excellent classification and generalization performance. However, most of the existing LDM-based models exhibit inherent flaws: (1) The assumption of a category uniform distribution, resulting in an inability to effectively address the issue of class imbalance. (2) The inheritance of the hinge loss in SVM, leading to inferior anti-noise performance. Given the shortcomings above, this paper constructs a novel K-means Triangular Synthesis (KTS) method for imbalanced data classification and introduces Unified Pinball (UP) loss to ensure robust performance, demonstrated as a novel KTS large margin classifier with UP Loss (KTS-UPLMC) model. Specifically, the KTS method utilizes the triangular synthesis and generates samples based on the cluster density, effectively mitigating the problems of introducing noise samples and strip-shaped sample distribution in the traditional synthetic minority oversampling technique, further alleviating the LDM’s classification line offset. In addition, the UP Loss considers quantile distance, improving the anti-noise performance of the model. Comparative experiments on artificial datasets and benchmark datasets validate the effectiveness and noise resistance of the proposed KTS-UPLMC in imbalanced data classification problems. Furthermore, the stability of the KTS-UPLMC is substantiated by the parameter sensitivity analysis experiment. In conclusion, the KTS method effectively addresses category imbalance, while the integration of the UP Loss enhances noise resistance.

Addressing Class Imbalance of Health Data: A Systematic Literature Review on Modified Synthetic Minority Oversampling Technique (SMOTE) Strategies

The Synthetic Minority Oversampling Technique (SMOTE) method is the baseline for solving unbalanced data problems. The working concept of the SMOTE method is to generate new synthetic data patterns by performing linear interpolation between minority class samples based on k-nearest neighbors. However, the SMOTE method has weaknesses, namely the problem of overgeneralization due to excessive sampling of sample noise and increased overlapping between classes in the decision boundary area, which has the potential for noise data. Based on the weaknesses of the Smote method, the purpose of this research is to conduct a systematic literature review on the Smote method modification approach in solving unbalanced data. This systematic literature review method comprises keyword identification, article search process, determination of selection criteria, and selection results based on criteria. The results of this study showed that the SMOTE modification approach was based on filtering, clustering, and distance modification to reduce the resulting noise data. The filtering approach removed the noise data before SMOTE, positively impacting resolving unbalanced data. Meanwhile, the use of a clustering approach in SMOTE can minimize the overlapping artificial minority data that has noise potential. The most used datasets are Pima 60% and Haberman 50%. The most used performance evaluation on unbalanced data is f1-measure 57%, accuracy 55%, recall 43%, and AUC 27%. The implication of the results of this literature review is to provide opportunities for further research in modifying SMOTE in addressing health data imbalances, especially handling noise and overlapping data. The thoroughness of our literature review should instill confidence in the research community.

Visual Perceptual Learning of Form-Motion Integration: Exploring the Involved Mechanisms with Transfer Effects and the Equivalent Noise Approach.

Background: Visual perceptual learning plays a crucial role in shaping our understanding of how the human brain integrates visual cues to construct coherent perceptual experiences. The visual system is continually challenged to integrate a multitude of visual cues, including form and motion, to create a unified representation of the surrounding visual scene. This process involves both the processing of local signals and their integration into a coherent global percept. Over the past several decades, researchers have explored the mechanisms underlying this integration, focusing on concepts such as internal noise and sampling efficiency, which pertain to local and global processing, respectively. Objectives and Methods: In this study, we investigated the influence of visual perceptual learning on non-directional motion processing using dynamic Glass patterns (GPs) and modified Random-Dot Kinematograms (mRDKs). We also explored the mechanisms of learning transfer to different stimuli and tasks. Specifically, we aimed to assess whether visual perceptual learning based on illusory directional motion, triggered by form and motion cues (dynamic GPs), transfers to stimuli that elicit comparable illusory motion, such as mRDKs. Additionally, we examined whether training on form and motion coherence thresholds improves internal noise filtering and sampling efficiency. Results: Our results revealed significant learning effects on the trained task, enhancing the perception of dynamic GPs. Furthermore, there was a substantial learning transfer to the non-trained stimulus (mRDKs) and partial transfer to a different task. The data also showed differences in coherence thresholds between dynamic GPs and mRDKs, with GPs showing lower coherence thresholds than mRDKs. Finally, an interaction between visual stimulus type and session for sampling efficiency revealed that the effect of training session on participants' performance varied depending on the type of visual stimulus, with dynamic GPs being influenced differently than mRDKs. Conclusion: These findings highlight the complexity of perceptual learning and suggest that the transfer of learning effects may be influenced by the specific characteristics of both the training stimuli and tasks, providing valuable insights for future research in visual processing.

Sustainable energy: Advancing wind power forecasting with grey wolf optimization and GRU models

Wind power forecasting is critical for optimizing energy use and ensuring the reliability of wind power systems in renewable energy. This paper introduces a novel method that combines the Grey Wolf Optimization (GWO) algorithm with data compression techniques to enhance feature selection and reduce redundancy in wind speed prediction. By employing GWO, essential features were identified by grouping the dataset into intervals and analyzing their frequencies. Performance evaluation was conducted using various compression measures, including Rate DC-Miss, Rate DC-MEF, and Rate DC-BDG, compared with other models such as extreme gradient boosting, space-time graph neural networks, and deep learning models. The study's results show significant improvements in accuracy and efficiency for predicting wind speed compared to existing techniques. The proposed approach addresses both larger datasets and the impact of noise samples on prediction errors. Additionally, an MLDDR model was introduced to predict DC power generated from wind datasets, encompassing five stages: Data Preparation, Feature Selection, Data Compression, GRU-Based Predictions, and Rate of Reduction. Data reduction results are notable. The original wind dataset (104857613) was reduced to 1093913 after processing missing values, achieving a reduction rate of 0.136. Applying the MEF-GWO algorithm further reduced the dataset to 109395, with a reduction rate of 0.385. The BDG dataset was compressed to 1805, with a reduction rate of 0.607. In terms of prediction performance, the GRU model was evaluated on three datasets: the original, MEF, and BDG-GWO datasets. The GRU model demonstrated the highest accuracy (99.20 %) with the BDG-GWO dataset, with precision (0.9965), recall (0.9978), and F1 scores (0.9897) indicating superior performance. Training and testing times varied significantly, highlighting the computational challenges associated with deep learning techniques. This research addresses both programming and application challenges. Programming challenges include high computational demands and the trial-and-error nature of parameter determination in deep learning, mitigated by using GWO. Application challenges involve reducing large datasets, grouping them into intervals, and evaluating performance using different compression measures. The main research questions addressed include the suitability of the GWO algorithm for dataset reduction in terms of dimensions (features and records) and the effectiveness of combining GWO with deep learning, specifically GRU, for enhanced prediction results. The study concludes that the GWO-PCA and GRU combination significantly improves prediction accuracy and reduces implementation time.

Coarse-to-Fine Structure and Semantic Learning for Single-Sample SAR Image Generation

Synthetic Aperture Radar (SAR) enables the acquisition of high-resolution imagery even under severe meteorological and illumination conditions. Its utility is evident across a spectrum of applications, particularly in automatic target recognition (ATR). Since SAR samples are often scarce in practical ATR applications, there is an urgent need to develop sample-efficient augmentation techniques to augment the SAR images. However, most of the existing generative approaches require an excessive amount of training samples for effective modeling of the SAR imaging characteristics. Additionally, they show limitations in augmenting the interesting target samples while maintaining image recognizability. In this study, we introduce an innovative single-sample image generation approach tailored to SAR data augmentation. To closely approximate the target distribution across both the spatial layout and local texture, a multi-level Generative Adversarial Network (GAN) architecture is constructed. It comprises three distinct GANs that independently model the structural, semantic, and texture patterns. Furthermore, we introduce multiple constraints including prior-regularized noise sampling and perceptual loss optimization to enhance the fidelity and stability of the generation process. Comparative evaluations against the state-of-the-art generative methods demonstrate the superior performance of the proposed method in terms of generation diversity, recognizability, and stability. In particular, its advantages over the baseline method are up to 0.2 and 0.22 in the SIFID and SSIM, respectively. It also exhibits stronger robustness in the generation of images across varying spatial sizes.

Probability based dynamic soft label assignment for object detection

By defining effective supervision labels for network training, the performance of object detectors can be improved without incurring additional inference costs. Current label assignment strategies generally require two steps: first, constructing a positive sample candidate bag, and then designing labels for these samples. However, the construction of candidate bag of positive samples may result in some noisy samples being introduced into the label assignment process. We explore a single-step label assignment approach: directly generating a probability map as labels for all samples. We design the label assignment approach from the following perspectives: Firstly, it should be able to reduce the impact of noise samples. Secondly, each sample should be treated differently because each one matches the target to a different extent, which assists the network to learn more valuable information from high-quality samples. We propose a probability-based dynamic soft label assignment method. Instead of dividing the samples into positive and negative samples, a probability map, which is calculated based on prediction quality and prior knowledge, is used to supervise all anchor points of the classification branch. The weight of prior knowledge in the labels decreases as the network improves the quality of instance predictions, as a way to reduce noise samples introduced by prior knowledge. By using continuous probability values as labels to supervise the classification branch, the network is able to focus on high-quality samples. As demonstrated in the experiments on the MS COCO benchmark, our label assignment method achieves 40.9% AP in the ResNet-50 under 1x schedule, which improves FCOS performance by approximately 2.0% AP. The code has been available at https://github.com/Liyi4578/PDSLA.

MRCFN: A multi-sensor residual convolutional fusion network for intelligent fault diagnosis of bearings in noisy and small sample scenarios

Bearing fault diagnosis is of great importance to ensure the safe and stable operation of mechanical equipment. The actual collected bearing fault signals are susceptible to strong noise interference and bearing samples for each fault state may be insufficient, which increases the difficulty of capturing effective features. Most of the existing diagnostic methods extract features from a single sensor signal for pattern recognition and fault diagnosis. The fault information provided by a single sensor is limited and incomplete, which is usually very difficult to meet the demand for accurate and reliable fault diagnosis in complex scenarios. To solve these problems, this paper proposes a multi-sensor residual convolutional fusion network (MRCFN) for intelligent fault diagnosis of bearings. Firstly, a convolutional pooling module (CPM) is coupled with the designed double ring residual module (DRRM) to rough feature extraction and deep feature mining, which not only captures the discriminative fault features from multi-sensor signal, but also avoids the performance degradation of network. Secondly, a spatial channel reconstruction module (SCRM) is further introduced to eliminate redundant information in the features and improve the network training efficiency. Finally, the presented global interactive perception fusion module (GIPFM) is connected with a classification block (CB) to globally fuse the features extracted from the acoustic and vibration signals and conduct automatic fault identification, which both can realize the complementarity and calibration of multi-sensor feature information and high precision diagnosis. The experiments and a series of comparisons are implemented on two datasets to efficaciously verify the superiority of the proposed method over five existing representative multi-sensor fusion diagnosis methods (i.e., FAC-CNN, MB-CNN, MsACNN, MRSDF, and IMSFDFL) under strong noise and small samples.

A novel wind power forecast diffusion model based on prior knowledge

AbstractTo improve the forecast accuracy of wind power, diffusion model based on prior knowledge (DMPK) is proposed. Different from the traditional diffusion model (DM), where the noise perturbation in the diffusion or generation process is random, the noise added in DMPK is modified aiming to the characteristics of wind power signals. The distribution of wind power forecast errors is not a standard Gaussian. Wind power forecast errors are related to forecast methods, weather conditions, and other factors, containing both random signals and certain regularity. This paper adapts the Gaussian distribution to fit the historical forecast error to represent the prior knowledge of wind power. Then, the sampling distribution is derived from its relationship with the fitted prior distribution to replace the standard Gaussian in DM. Taking the prior knowledge into account during the process of noise sampling, the data in the forward process of DMPK can be guided by the distribution of historical errors for diffusion, while the generated result by the reverse process is more consistent with the actual wind power signal. Finally, the superiority of the proposed method is verified by using the wind power data from two real‐world wind farms.

A high-quality self-supervised image denoising method based on SDDW-GAN and CHRNet

Image denoising remains a classic and crucial issue in the field of image processing, significantly impacting the outcomes of subsequent image processing tasks. For instance, the denoising network depends on “noise-clean” image pairs to train network effectively. However, it is often hampered by issues such as useful information loss, low training efficiency, and poor blind denoising. To address these challenges, this study proposes a novel image denoising network that integrates the complementary strengths of model-based and learning-based approaches, specifically leveraging the capabilities of both SDDW-GAN and CHRNet. Firstly, SDDW-GAN is designed to estimate the noise distribution on the input noisy images, and a fast-smoothing noisy block sampling algorithm is proposed to extract the noise blocks in noisy images in SDDW-GAN. Secondly, a network with dual generators and dual discriminators based on W-GAN is designed to estimate the noise distribution on the input noisy images and generate noise sample pairs with the same noise distribution, which solves the problem of relying on “noise-clean” image pairs. Thirdly, CHRNet is designed to compute the mapping relationship between the double-noise samples and the single-noise samples. In order to further improve the denoising effect, the dual-channel residual attention module is proposed for fusion learning of global and local features. Experimental results show that the proposed method has a better denoising effect in complex environments and outperforms existing denoising methods. Specifically, in comparison with the stand-alone denoising methods BM3D, DnCNN, Noise2Noise, and Blind2Unblind, the proposed method improves the average peak signal-to-noise ratio (PSNR) by 0.23 dB to 0.78 dB on two benchmarking datasets crossing different noise levels. Its denoising effect is also greater than other competitive stand-alone and combination methods. The proposed method can also extend to low-light image enhancement, deblurring, and super-resolution.

Acoustic signal adversarial augmentation for pressure pipeline leakage detection

Pressure pipelines are prone to leakage under harsh working condition for a long time, and the leakage detection reaches unsatisfactory performance due to influence of background noise and insufficient sample for acoustic signals. Therefore, the acoustic signals adversarial augmentation method is proposed for pressure pipeline leakage detection based on noise reduction and sample generation. By deeply connecting with generative adversarial network (GAN), denoising autoencoder (DAE) and residual network (ResNet), the adversarial denoising and generation model (ADGM) is established to reduce the noise of acoustic signal. In addition, the trained DAE of ADGM is applied to augment the acoustic samples, thereby completing adversarial augmentation of acoustic signal, which is significant for pressure pipeline leakage detection. Besides, the pipeline leakage experiment is implemented to validate the proposed method on noise reduction and sample generation, which can reach pressure pipeline detection accuracy of 93.02% based on augmented acoustic signal. Further, the effectiveness and superiority of the proposed method are tested by ablation experiments and comparative methods.

Automating the discovery of partial differential equations in dynamical systems

Abstract Identifying partial differential equations (PDEs) from data is crucial for understanding the governing mechanisms of natural phenomena, yet it remains a challenging task. We present an extension to the ARGOS framework, ARGOS-RAL, which leverages sparse regression with the recurrent adaptive lasso to identify PDEs from limited prior knowledge automatically. Our method automates calculating partial derivatives, constructing a candidate library, and estimating a sparse model. We rigorously evaluate the performance of ARGOS-RAL in identifying canonical PDEs under various noise levels and sample sizes, demonstrating its robustness in handling noisy and non-uniformly distributed data. We also test the algorithm’s performance on datasets consisting solely of random noise to simulate scenarios with severely compromised data quality. Our results show that ARGOS-RAL effectively and reliably identifies the underlying PDEs from data, outperforming the sequential threshold ridge regression method in most cases. We highlight the potential of combining statistical methods, machine learning, and dynamical systems theory to automatically discover governing equations from collected data, streamlining the scientific modeling process.

Correcting Fabry-Pérot etalon effects in solar observations

Context. Data processing pipelines of Fabry–Pérot interferometers (FPI) must take into account the side effects these devices introduce in the observations. Interpretation of these observations without proper correction can lead to inaccurate or false results, with consequent impact on their physical interpretation. Corrections typically require prior knowledge of the properties of the etalon and the way they affect the incoming light in order to calibrate the data successfully. Aims. We have developed an algorithm to derive etalon properties from flat-field observations and tested its applicability and accuracy using simulated observations and real measurements. Methods. We employed analytical expressions of the transmission profiles for FPIs in collimated and telecentric configurations to derive their expected impact on the observations. These analytical expressions allowed us to develop a customized optimization algorithm capable of inferring the properties of the etalon from the observations. The algorithm’s performance has been tested on simulated observations with an etalon in collimated and telecentric setups employing various noise levels and spectral samplings. Additionally, we explored how tilting the etalon in a telecentric configuration influences the algorithm’s effectiveness. Lastly, we also applied the algorithm to a set of real flat-field observations taken with the high-resolution telescope of the Polarimetric and Helioseismic Imager on board the Solar Orbiter mission (HRT-SO/PHI). Results. The algorithm is able to retrieve the gain and etalon induced transmission velocity shifts (cavity map), with an average accuracy ranging between 0.4% and 0.1% for the former and between 120 m s−1 and 30 m s−1 for the latter. Both reducing the noise level and increasing the spectral sampling of the observations proved to greatly increase the algorithm’s performance, as expected. Results also suggest that determination of the observed object from the data is possible but an additional error between 40 m s−1 and 10 m s−1 is to be expected in the inferred cavity map. Furthermore, we show that neglecting the asymmetries arising from either tilts of the etalon or imperfections in the telecentrism can lead to large errors when determining the gain. Tests with HRT-SO/PHI data have verified the applicability of the algorithm in real cases. Conclusions. Our presented method enabled us to derive the transmission profile of FPIs from observations of collimated and telecentric configurations. It has proven to be robust against the presence of noise and limited spectral line sampling. The results reported here also show the importance of accounting for the asymmetries arising in real telecentric mounts when interpreting the results of real instruments.

Study on the sound quality of the electric vehicle powertrain under acceleration conditions

The evaluation and prediction of the sound quality (SQ) of electric vehicle (EV) powertrains are critical to the overall SQ of EVs. Firstly, a grouping method for noise samples is proposed to achieve rational grouping when SQ evaluations are performed using the grouped paired comparison method and its improvements. Secondly, aiming at the limitations of psychoacoustic parameters in predicting SQ under nonstationary conditions, a SQ prediction model based on the energy features of intrinsic mode functions (IMF) of signals is proposed. Finally, SQ evaluations are conducted, comparing the prediction performance of two SQ models based on energy features and psychoacoustic parameters. The prediction results show that the mean absolute percentage error (MAPE) of the model with energy features is 4.18%, while the MAPE of the model with psychoacoustic parameters is 8.88%, which demonstrates that energy features are superior in predicting the SQ of EV powertrains under acceleration conditions.

TEM-SDRF: A Novel Approach for Machinery Fault Diagnosis Amid Dual Imbalances in Fault and Noise Samples

TEM-SDRF: A Novel Approach for Machinery Fault Diagnosis Amid Dual Imbalances in Fault and Noise Samples

Sampling effects on background noise corrections

Background noise corrections are used to estimate true source levels from measured source levels. Use of a background correction involves determining the background noise from measured samples. Previous work explored the effect of sampling on the statistical background noise correction through the steadiness parameter. In this paper the effect of sampling on the statistical and legacy background noise corrections through the steadiness and source-background level difference parameters is investigated. Samples of background noise are drawn from a chi-square distribution, which has been shown to describe background noise arising from multi-path or multi-mode processes. Background mean and variance are determined from a specified number of background noise samples, and statistical distributions and confidence bands are formed. The distributions and confidence bands of the statistical and legacy background noise corrections are compared and are used to identify parameter ranges that promote accuracy of source levels obtained with background noise corrections. Specific limits on these parameters are recommended for the statistical background noise correction. The benefits and advantages of the statistical background noise correction are identified, and a path towards standardizing the statistical background noise correction is proposed.