Categories Of Noise Research Articles

DeepMatch: Toward Lightweight in Point Cloud Registration.

From source to target, point cloud registration solves for a rigid body transformation that aligns the two point clouds. IterativeClosest Point (ICP) and other traditional algorithms require a long registration time and are prone to fall into local optima. Learning-based algorithms such as Deep ClosestPoint (DCP) perform better than those traditional algorithms and escape from local optimality. However, they are still not perfectly robust and rely on the complex model design due to the extracted local features are susceptible to noise. In this study, we propose a lightweight point cloud registration algorithm, DeepMatch. DeepMatch extracts a point feature for each point, which is a spatial structure composed of each point itself, the center point of the point cloud, and the farthest point of each point. Because of the superiority of this per-point feature, the computing resources and time required by DeepMatch to complete the training are less than one-tenth of other learning-based algorithms with similar performance. In addition, experiments show that our algorithm achieves state-of-the-art (SOTA) performance on both clean, with Gaussian noise and unseen category datasets. Among them, on the unseen categories, compared to the previous best learning-based point cloud registration algorithms, the registration error of DeepMatch is reduced by two orders of magnitude, achieving the same performance as on the categories seen in training, which proves DeepMatch is generalizable in point cloud registration tasks. Finally, only our DeepMatch completes 100% recall on all three test sets.

End-to-end sensor fusion and classification of atrial fibrillation using deep neural networks and smartphone mechanocardiography

Objective. The purpose of this research is to develop a new deep learning framework for detecting atrial fibrillation (AFib), one of the most common heart arrhythmias, by analyzing the heart’s mechanical functioning as reflected in seismocardiography (SCG) and gyrocardiography (GCG) signals. Jointly, SCG and GCG constitute the concept of mechanocardiography (MCG), a method used to measure precordial vibrations with the built-in inertial sensors of smartphones. Approach. We present a modified deep residual neural network model for the classification of sinus rhythm, AFib, and Noise categories from tri-axial SCG and GCG data derived from smartphones. In the model presented, pre-processing including automated early sensor fusion and spatial feature extraction are carried out using attention-based convolutional and residual blocks. Additionally, we use bidirectional long short-term memory layers on top of fully-connected layers to extract both spatial and spatiotemporal features of the multidimensional SCG and GCG signals. The dataset consisted of 728 short measurements recorded from 300 patients. Further, the measurements were divided into disjoint training, validation, and test sets, respectively, of 481 measurements, 140 measurements, and 107 measurements. Prior to ingestion by the model, measurements were split into 10 s segments with 75 percent overlap, pre-processed, and augmented. Main results. On the unseen test set, the model delivered average micro- and macro-F1-score of 0.88 (0.87–0.89; 95% CI) and 0.83 (0.83–0.84; 95% CI) for the segment-wise classification as well as 0.95 (0.94–0.96; 95% CI) and 0.95 (0.94–0.96; 95% CI) for the measurement-wise classification, respectively. Significance. Our method not only can effectively fuse SCG and GCG signals but also can identify heart rhythms and abnormalities in the MCG signals with remarkable accuracy.

Ideal ratio mask estimation using supervised DNN approach for target speech signal enhancement

The most challenging process in recent Speech Enhancement (SE) systems is to exclude the non-stationary noises and additive white Gaussian noise in real-time applications. Several SE techniques suggested were not successful in real-time scenarios to eliminate noises in the speech signals due to the high utilization of resources. So, a Sliding Window Empirical Mode Decomposition including a Variant of Variational Model Decomposition and Hurst (SWEMD-VVMDH) technique was developed for minimizing the difficulty in real-time applications. But this is the statistical framework that takes a long time for computations. Hence in this article, this SWEMD-VVMDH technique is extended using Deep Neural Network (DNN) that learns the decomposed speech signals via SWEMD-VVMDH efficiently to achieve SE. At first, the noisy speech signals are decomposed into Intrinsic Mode Functions (IMFs) by the SWEMD Hurst (SWEMDH) technique. Then, the Time-Delay Estimation (TDE)-based VVMD was performed on the IMFs to elect the most relevant IMFs according to the Hurst exponent and lessen the low- as well as high-frequency noise elements in the speech signal. For each signal frame, the target features are chosen and fed to the DNN that learns these features to estimate the Ideal Ratio Mask (IRM) in a supervised manner. The abilities of DNN are enhanced for the categories of background noise, and the Signal-to-Noise Ratio (SNR) of the speech signals. Also, the noise category dimension and the SNR dimension are chosen for training and testing manifold DNNs since these are dimensions often taken into account for the SE systems. Further, the IRM in each frequency channel for all noisy signal samples is concatenated to reconstruct the noiseless speech signal. At last, the experimental outcomes exhibit considerable improvement in SE under different categories of noises.

Noise Exposure and Use of Hearing Protection Among Adolescents in Rural Areas.

Noise-induced hearing loss (NIHL) has been found in rural children, potentially due to occupational and recreational noise exposure without consistent use of hearing protection devices (HPDs). However, questions remain regarding the specifics of rural adolescents' noise exposure and use of hearing protection around different types of noise. As such, the purpose of the current study was to provide preliminary results on rural adolescents' noise exposure and use of hearing protection for gunfire, heavy machinery, power tools, all-terrain vehicles (ATVs), and music. A questionnaire was administered to 197 students (seventh to 12th grade) from rural schools in West Texas. Questions were related to noise exposure and use of HPDs for specific categories of noise. Testing was performed at the schools, with an investigator recording each student's responses. Approximately 18%-44% of adolescents reported exposure 12 or more times a year to gunfire, heavy machinery, power tools, and ATVs. Only 1%-18% of the adolescents reported never being exposed to such noise sources. Almost half of rural adolescents never used hearing protection around gunfire, and 77%-91% reported never wearing hearing protection when exposed to heavy machinery, power tools, and ATVs. The current study revealed that rural adolescents are exposed to noise sources that could damage their hearing. However, the majority of rural adolescents do not consistently wear hearing protection. Additional research is now needed to extend these findings by assessing rural adolescents' duration of exposure to different noise sources, in addition to investigating prevention of NIHL in this population. Supplemental Material https://doi.org/10.23641/asha.17139335.

Application of Noise Certification Regulations within Conceptual Aircraft Design

ICAO Annex 16 regulations are used to certify the acoustic performance of subsonic transport aircraft. Each aircraft is classified according to the measured EPNL levels at specific certification locations along the approach and departure. By simulating this certification process, it becomes possible to identify all relevant parameters and assess promising measures to reduce the noise certification levels in compliance with the underlying ICAO regulations, i.e., allowable operating conditions of the aircraft. Furthermore, simulation is the only way to enable an assessment of novel technology and non-existing vehicle concepts, which is the main motivation behind the presented research activities. Consequently, the ICAO Annex 16 regulations are integrated into an existing noise simulation framework at DLR, and the virtual noise certification of novel aircraft concepts is realized at the conceptual design phase. The predicted certification levels can be directly selected as design objectives in order to realize an advantageous ICAO noise category for a new aircraft design, i.e., simultaneously accounting for the design and the resulting flight performance. A detailed assessment and identification of operational limits and allowable flight procedures for each conceptual aircraft design under consideration is enabled. Sensitivity studies can be performed for the relevant input parameters that influence the predicted noise certification levels. Specific noise sources with a dominating impact on the certification noise levels can be identified, and promising additional low-noise measures can be applied within the conceptual design phase. The overall simulation process is applied to existing vehicles in order to assess the validity of the simulation resultsfcompared to published data. Thereafter, the process is applied to some DLR low-noise aircraft concepts to evaluate their noise certification levels. These results can then be compared to other standard noise metrics that are typically applied in order to describe aircraft noise, e.g., SEL isocontour areas. It can be demonstrated that certain technologies can significantly reduce the noise impact along most of an approach or departure flight track but have only a limited influence on the noise certification levels and vice versa. Finally, an outlook of the ongoing developments is provided, in order to apply the new simulation process to supersonic aircraft. Newly proposed regulations for such concepts are implemented into the process in order to evaluate these new regulations and enable direct comparison with existing regulations.

Urban Noise Inference Model Based on Multiple Views and Kernel Tensor Decomposition

Prediction of urban noise is becoming more significant for tackling noise pollution and protecting human mental health. However, the existing noise prediction algorithms neglected not only the correlation between noise regions, but also the nonlinearity and sparsity of the data, which resulted in low accuracy of filling in the missing entries of data. In this paper, we propose a model based on multiple views and kernel-matrix tensor decomposition to predict the noise situation at different times of day in each region. We first construct a kernel tensor decomposition model by using kernel mapping in order to speed decomposition rate and realize stable estimate the prediction system. Then, we analyze and compute the cause of the noise from multiple views including computing the similarity of regions and the correlation between noise categories by kernel distance, which improves the credibility to infer the noise situation and the categories of regions. Finally, we devise a prediction algorithm based on the kernel-matrix tensor factorization model. We evaluate our method with a real dataset, and the experiments to verify the advantages of our method compared with other existing baselines.

Does pre-processing affect the correlation indicator between Twitter message volume and stock market trading volume?

Motivation: More and more authors empirically verify the relationship between the volume of tweets and the stock market indicators. The patterns explored from Twitter most often take the form of time series that represent user’s activity on different level of granularity (moods, emotions, relevant topic or query-related messages). Sentiment analysis is a technique used to transform text data into information on the mood and related behavioral categories. Supervised machine learning is the most commonly used approach to sentiment analysis. Thus, the results of an empirical analysis of the relationship between social media and stock depend on the quality of results of classification task. The quality of the features used to learn the classifier plays a key role. The feature space is modified using various data pre-processing scenarios that aim to increase accuracy of classification. The impact of pre-processing data on the quality of classification is often discussed in studies. Very few authors discuss the impact of pre-processing on the correlation indicator between Twitter and stock market. Aim: Analysis of the impact of tweets pre-processing on the Pearson correlation indicator between the mood of Twitter users and stock market trading volume. Results: The correlation between the volume of stock market trading and the volume of tweets has been empirically confirmed. The effect of pre-processing on the correlation index was noted for the variables ‘all_tweets’ and ‘negative_tweets’. This is because the training set has a significant amount of tweets with negation. However, the results are not conclusive. The differences between the Pearson correlation index calculated for scenario one and scenario four are not significant. However, this indicates that the effect of noise data may reduce the quality and precision of conclusions. Especially in the case of frequent repetition of a certain category of noise.

The Use of Saliency in Underwater Computer Vision: A Review

Underwater survey and inspection are tasks of paramount relevance for a variety of applications. They are usually performed through the employment of optical and acoustic sensors installed aboard underwater vehicles, in order to capture details of the surrounding environment. The informative properties of the data are systematically affected by a number of disturbing factors, such as the signal energy absorbed by the propagation medium or diverse noise categories contaminating the resulting imagery. Restoring the signal properties in order to exploit the carried information is typically a tough challenge. Visual saliency refers to the computational modeling of the preliminary perceptual stages of human vision, where the presence of conspicuous targets within a surveyed scene activates neurons of the visual cortex, specifically sensitive to meaningful visual variations. In relatively recent years, visual saliency has been exploited in the field of automated underwater exploration. This work provides a comprehensive overview of the computational methods implemented and applied in underwater computer vision tasks, based on the extraction of visual saliency-related features.

Occupational Exposures and Ischaemic Heart Disease: Results from The Entire New Zealand Population

IntroductionIschaemic Heart Disease (IHD) is a leading cause of death in Western countries. Common occupational exposures such as loud noise, long working hours, and sedentary work have been associated with increased IHD risks, but inconsistently.
 Objectives and ApproachThis study examines associations between incident IHD and exposure to long working hours, sedentary work, and loud noise. Individual-level microdata from Statistics New Zealand Integrated Data Infrastructure (IDI) were extracted for adults (age 20-64 years) with occupation recorded on the 2013 Census. The number of working hours was extracted from the Census, and exposure to sedentary work and loud noise was assessed through job exposure matrices (JEMs). IHD events (from 2013 to end of 2018) were identified using hospitalisations, prescriptions and deaths. Hazard ratios (HRs) were calculated using cox regression adjusted for age, socioeconomic status, and smoking. Results were stratified by sex and ethnicity.
 ResultsA total of 20,610 IHD cases were identified from 1,594,680 individuals employed at time of Census. Both short (<35) and long (55+) working hours were associated with an increased IHD risk in crude analyses, but effects disappeared after adjustment for age and socioeconomic status. For females, sedentary work (>90% of the time compared to <50%) was associated with a reduced risk (HR(Non-Māori)=0.86, 95%CI=0.75-0.99; HR(Māori)=0.71, 95%CI=0.44-1.14). For males, exposure to the highest noise category (>90dBA) compared to no exposure (<80dBA) was associated with elevated HRs without reaching statistical significance (HR(Non-Māori)=1.12, 95%CI=0.96-1.29; HR(Māori)=1.25, 95%CI=0.89-1.75). For females exposure to the 80-85dBA category compared to no exposure also showed elevated HRs (HR(Non-Māori)=1.14; 95%CI=1.04-1.26; HR(Māori)=1.16; 95%CI=0.93-1.46), but too few females were employed in jobs with the highest noise exposure.
 ConclusionThese preliminary analyses do not support sedentary work or long working hours as IHD risk factors, but do suggest a modest increase in IHD risk associated with occupational exposure to noise.

ECG arrhythmia classification by using a recurrence plot and convolutional neural network

Cardiovascular diseases affect approximately 50 million people worldwide; thus, heart disease prevention is one of the most important tasks of any health care system. Despite the high popularity electrocardiography, superior automatic electrocardiography (ECG) signal analysis methods are required. The aim of this research was to design a new deep learning method for effectively classifying arrhythmia by using 2-second segments of 2D recurrence plot images of ECG signals. In the first stage, the noise and ventricular fibrillation (VF) categories were distinguished. In the second stage, the atrial fibrillation (AF), normal, premature AF, and premature VF categories were distinguished. Models were trained and tested using ECG databases publicly available at the website of PhysioNet. The MIT-BIH Arrhythmia Database, Creighton University Ventricular Tachyarrhythmia Database, MIT-BIH Atrial Fibrillation Database, and MIT-BIH Malignant Ventricular Ectopy Database were used to compare six types of arrhythmia. Testing accuracies of up to 95.3 % ± 1.27 % and 98.41 % ± 0.11 % were achieved for arrhythmia detection in the first and second stage, respectively, after five-fold cross-validation. In conclusion, this study provides clinicians with an advanced methodology for detecting and discriminating between different arrhythmia types.

Distributed filtering for Markov jump systems with randomly occurring one-sided Lipschitz nonlinearities under Round-Robin scheduling

This paper focuses on distributed filter design for Markov jump one-sided Lipschitz nonlinear systems over a sensor network by applying Round-Robin scheduling scheme. The nonlinear perturbations occur randomly and satisfy the so-called one-sided Lipschitz constraint that is more general than the conventional Lipschitz restraint. Two categories of exogenous noises, i.e., the persistent bounded noise and square additive noise in l2 space, are taken into investigation, respectively. Meanwhile, the well-known Round-Robin scheduling protocol is employed to regulate the data transmission through the shared communication media. The exponential mean-square ultimate boundedness and stochastic bounded real lemma (BRL) are obtained for the filtering error dynamics, and then two distributed filters are correspondingly designed. Finally, an illustrative example shows the effectiveness of the proposed results.

Implementation Of underwater target tracking techniques for Gaussian and non-Gaussian environments

There are several techniques implemented, in an underwater target tracking environment, for the nonlinear dynamic systems in Gaussian and non-Gaussian environments. It is assumed with non-Gaussian distribution to make the problem part of the non-Gaussian distribution, and is measured in terms of calculations of plenty of scenarios simulated to validate the potential of the sub-optimal filter.This research is further carried out by considering two categories of non-Gaussian noises i.e.a mixture of Gaussian noises and shot noise. To evaluate tracking in Gaussian and non-Gaussian noises, the suboptimal filters, Extended Kalman filter, and Unscented Kalman filter (UKF algorithms are considered. Gaussian noise is a statistical noise having probability density function equal to the normal distribution function. The suboptimal filters, Extended Kalman filter, and Unscented Kalman filter (UKF) algorithms are considered to evaluate tracking in Gaussian and non-Gaussian noises. To make further evaluation of the above said algorithms, they are compared with theoretical Cramer-Rao lower bound. The efficiency of UKF is in terms of percentage of non-Gaussian noise corrupted measurements, for which solution is obtained within a short time. The application of Monte-Carlo method at this simulations trapped accurate results.

EMG Signal Filtering Based on Variational Mode Decomposition and Sub-Band Thresholding.

Surface electromyography (EMG) signals are inevitably contaminated by various noise components, including powerline interference (PLI), baseline wandering (BW), and white Gaussian noise (WGN). These noises directly degrade the efficiency of EMG processing and affect the accuracy and robustness of further applications. Currently, most of the EMG filters only target one category of noise. Here, we propose a novel filter to remove all three types of noise. The noisy EMG signal is first decomposed into an ensemble of band-limited modes using variational mode decomposition (VMD). Each category of noise is located within specific modes and is separately removed in sub-bands. In particular, WGN is suppressed by soft thresholding with a noise level-dependent threshold. The denoising performance was assessed from simulated and experimental signals using three performance metrics: the root mean square error ([Formula: see text]), the improvement in signal-to-noise ratio ([Formula: see text]), and the percentage reduction in the correlation coefficient ( η). Other methods, including traditional infinite impulse response (IIR) filters, empirical mode decomposition (EMD) method, and ensemble empirical mode decomposition (EEMD) method, were examined for comparison. The proposed method achieved the best performance to remove BW or WGN. It also effectively reduced PLI noise when the signal-to-noise ratio (SNR) was low. The SNR was improved by 18.6, 19.2, and 8.0dB for EMG signals corrupted with PLI, BW, and WGN at -6dB SNR, respectively. The experimental results illustrated that noise was completely removed from resting states, and obvious spikes were distinguished from action states. For two of the ten subjects, the improved SNR reached 20dB. This study explores the special characteristics of VMD and demonstrates the feasibility of using the VMD-based filter to denoise EMG signals. The proposed filter is efficient at removing three categories of noise and can be used for any application that requires EMG signal filtering at the preprocessing stage, such as gesture recognition and EMG decomposition.

Examining the Utility of Perceptual Noise Categorization in Pediatric and Neonatal Hospital Units.

Medical and nursing staff working in hospitals often experience exposure to extreme sound environments, and there is growing evidence of the negative impacts. Previous research highlighted various complexities regarding noise sources in hospitals; however, identifications of intrinsic noise categories that can reveal the complex mixture of existing hospital noise is still limited. The objective of this work was to identify intrinsic categories of the noise sources based on staff perceived annoyance and explore clear associations of these categorized noise sources with psychological perceptions. The staff perceptual responses regarding hospital noise were assessed by conducting surveys at the three pediatric and neonatal care units in two hospitals. Using principle component analysis (PCA), the psychological annoyance responses of 94 participants were used to derive the inherent structural patterns of the existing noise sources. The derived PCA categorization was validated on mixed-model analysis of variances, and employed on regression models to explore potential associations between the categorized noise factors and the staff's psychological perceptions. The results highlighted three intrinsic noise categories and their negative impacts on staff's psychological perceptions including work/rest disturbance and noisiness. Taken as a whole, the findings better reveal problematic noise source categories and establish a framework for hospital noise control that is less source-specific and more broadly generalizable.

Urban Noise Conflicts in Szeged

Noise pollution is a serious and complex city problem. While there are objective, measurable parameters for noise mapping assessments, since the effects of noise are also subjectively perceived, it has been difficult to accurately evaluate the urban problems and conflicts arising from noise. Urban noise and its perception is related to the quality of life; thus its analysis is can provide useful insights for decision-makers. Therefore, through an analysis of online media content, the paper presents local the attitudes in Szeged towards urban noise. During the analysis different noise categories and the city’s noise characteristics were determined. Even though the noise pollution in residential areas was found to be mainly concentrated in the city centre, it also affected more remote areas, and social problems and political discourses were also identified. Besides the noise of urban traffic, Szeged people appeared to be disturbed by noise related to leisure activities, such as urban (and university) programs and festival noise, which indicated that noise reduction efforts should be focus on more than just the reduction of traffic noise.

Effective satellite image enhancement based on the discrete wavelet transform

Satellite image enhancement and restoration is scientifically possible by applying image processing and other soft computing techniques. In this study, a comparison of various existing satellite image resolution enhancement techniques in wavelet domain has been done. I propose a new satellite image enhancement technique based on DCWT-GWO. The novel technique employs the duel tree complex wavelets which, in relation to the classical wavelets, permit a faster execution of the wavelet transforms and furnish incredible flexibility in the generation of the wavelets. An effective system for noise reduction have designed in the backdrop of diverse categories of noise, where an optimal threshold is chosen for each sub band at diverse scales by means of the Gray Wolf optimisation (GWO) algorithm. The optimal threshold will denoise the image effectively. The fitness of the optimisation algorithm determine the quality of the image.

Can Terrestrial Restoration Methodologies be Transferred to Planetary Hyperspectral Imagery? A Quantitative Intercomparison and Discussion

Hyperspectral imaging is a significant remote sensing technology for deep space exploration to understand the planetary geological evolution. However, hyperspectral images (HSIs) usually suffer from various noises because of the complicated environment and equipment limitations, which leads to inconvenience to subsequent applications. In this work, a comprehensive and systematic investigation on planetary noise categories is summarized initially, and an intercomparison among state-of-the-art terrestrial joint spatial-spectral restoration models is performed to test their capability on planetary datasets. The Compact Reconnaissance Imaging Spectrometer for Mars and Observatoire pour la Mineralogie, l'Eau, les Glaces et l'Activite are adopted as examples. An improved nonreference quantitative evaluation method based on the high-resolution imaging science experiment imagery is proposed. The processed high-resolution classification result of Russell Dune can be used as the reference of unmixing after denoising. Then, spectral and spatial fidelity can be assessed indirectly. Experimental results emphasize that denoising approaches with modeling for non-independently and identically (non i.i.d.) noise characteristics are more suitable for planetary HSIs because of the diversity and complexity of their noises. This kind of method is flexible under practical circumstances and maintains the intrinsic information of HSIs better.

Identification of open quantum system: Via closed feedback control

For stochastic quantum systems with given dissipations, the identification method of Hamiltonian is given in this paper. First, the stability of the system is realized through designing a real-time feedback control method. Second, by using Routh–Hurwitz stability criterion, the intervals of the element values of the system Hamiltonians are given. Finally, the identification of Hamiltonian is realized by selecting the equilibrium state of the system equal to the desired target state. For two typical categories of noise: Purely dephasing decoherence and amplitude damping decoherence, we check the validity of the proposed estimation scheme.

Fear of noises affects canine problem solving behavior and locomotion in standardized cognitive tests

As part of a larger problem solving study, 22 dogs who were identified by owners/clients as reacting to noise and 13 identified by owners/clients as not reacting to noise that had been previously compared for auditory function were compared with respect to their reported noise reactions, performance on a puzzle box test, response to a provocative noise recording, and movement. Each dog was evaluated using a standardized, validated, semi-quantitative objective questionnaire from which an Anxiety Intensity Rank (AIR) score was calculated. AIR score calculations reflect the number of categories of noise to which the dog reacts, the behaviors exhibited, and the intensity of the reaction. Each dog underwent a 13 item problem-solving test (The Canine Intelligence Test Protocol; CITP) designed to evaluate 4 standardly evaluated cognitive domains. We report on two of the 13 tests – the puzzle box test and the provocative noise test – for this group of dogs. During testing most dogs wore collars containing accelerometers using custom firmware which provided second-by-second 3D movement data. AIR scores for the 2 groups differed significantly (Welch’s t tests; t = 4.34, df = 19.23, P < 0.0004), although the affected group was only mildly affected. Affected dogs took longer to solve the tasks and, overall, did more poorly (P < 0.5). Accelerometry revealed that during testing, movements of affected dogs were more erratic, less continuous and subject to greater extreme deviations and longer pauses than were the movements of unaffected dogs. Even dogs mildly affected with fear of noises differed from unaffected dogs, and performed more poorly on problem-solving tests possibly, in part, because their movements were characterized by a high degree of physical and behavioral/emotional reactivity. Reactions to noise affect how these dogs move, which may affect every investigatory and interactive aspect of their lives. Combining AIR scores with movement measures may be a useful method to assess welfare in pet dogs.

A novel phylogenetic approach for de novo discovery of putative nuclear mitochondrial (pNumt) haplotypes

Current approaches for parsing true variation (i.e. signal) from noise, broadly involve estimating a baseline value of the latter, below which all sequence data are ignored. In an effort to deliver a more objective criterion for setting such thresholds, a novel approach based on phylogenetic principles is presented here., Our method deconstructs a special category of noise from true mitochondrial genome data, namely nuclear insertions of mitochondrial DNA (Numts). This bioinformatic approach leverages the relationship of massively parallel sequence reads and is capable of discovering putative Numts (pNumts) in absence of a reference genome. The new method was tested on a whole mitochondrial genome dataset (n = 41 individuals from an admixed population sample from Rio de Janeiro) and led to the discovery of 451 pNumt variants. Comparison of these pNumts haplotypes against an existing Numt database revealed 147 exact matches to previously discovered Numts, while 122 haplotypes differed only by a single base pair and none matched exclusively to the mitochondrial genome. In general, these sequences were considerably more divergent from the mitochondrial genome than from those of the Numt database, supporting that the novel pNumts were probably hitherto uncatalogued variants. Unlike previous techniques, our method appears to be able to detect both polymorphic and fixed Numt sequences. It was also found that the region containing the D-Loop and associated Promoters (DLP) in the human mitochondrial genome, which harbors markers of forensic genetics importance, is the origin of several Numts. Though currently designed for the mitochondrial genome, our novel approach has the potential to be expanded to other scenarios that might require construing signal from noise, including the deconvolution of mixtures, thus significantly improving how analytical thresholds may be established.