Showing papers on "Noise published in 2007"

Responses of cetaceans to anthropogenic noise

Abstract: 1 Since the last thorough review of the effects of anthropogenic noise on cetaceans in 1995, a substantial number of research reports has been published and our ability to document response(s), or the lack thereof, has improved. While rigorous measurement of responses remains important, there is an increased need to interpret observed actions in the context of population-level consequences and acceptable exposure levels. There has been little change in the sources of noise, with the notable addition of noise from wind farms and novel acoustic deterrent and harassment devices (ADDs/AHDs). Overall, the noise sources of primary concern are ships, seismic exploration, sonars of all types and some AHDs. 2 Responses to noise fall into three main categories: behavioural, acoustic and physiological. We reviewed reports of the first two exhaustively, reviewing all peer-reviewed literature since 1995 with exceptions only for emerging subjects. Furthermore, we fully review only those studies for which received sound characteristics (amplitude and frequency) are reported, because interpreting what elicits responses or lack of responses is impossible without this exposure information. Behavioural responses include changes in surfacing, diving and heading patterns. Acoustic responses include changes in type or timing of vocalizations relative to the noise source. For physiological responses we address the issues of auditory threshold shifts and ‘stress’, albeit in a more limited capacity; a thorough review of physiological consequences is beyond the scope of this paper. 3 Overall, we found significant progress in the documentation of responses of cetaceans to various noise sources. However, we are concerned about the lack of investigation into the potential effects of prevalent noise sources such as commercial sonars, depth finders and fisheries acoustics gear. Furthermore, we were surprised at the number of experiments that failed to report any information about the sound exposure experienced by their experimental subjects. Conducting experiments with cetaceans is challenging and opportunities are limited, so use of the latter should be maximized and include rigorous measurements and or modelling of exposure.

CLEAN Based on Spatial Source Coherence

Abstract: To obtain higher resolution acoustic source plots from microphone array measurements, deconvolution techniques are becoming increasingly popular. Deconvolution algorithms aim at identifying Point Spread Functions (PSF) in source plots, and may therefore fall short when actual beam patterns of measured noise sources are not similar to synthetically obtained PSF's. To overcome this, a new version of the classical deconvolution method CLEAN is proposed here: CLEAN-SC. By this new method, which is based on spatial source coherence, side lobes can be removed of actually measured beam patterns. Essentially, CLEAN-SC iteratively removes the part of the source plot which is spatially coherent with the peak source. A feature of CLEAN-SC is its ability to extract absolute sound power levels from the source plots. The merits of CLEAN-SC were demonstrated using array measurements of airframe noise on a scale model of the Airbus A340 in the 8×6 m2 closed test section of DNW-LLF.

Fat quantification with IDEAL gradient echo imaging: correction of bias from T(1) and noise.

Abstract: Quantification of hepatic steatosis is a significant unmet need for the diagnosis and treatment of patients with nonalcoholic fatty liver disease (NAFLD). MRI is capable of separating water and fat signals in order to quantify fatty infiltration of the liver (hepatic steatosis). Unfortunately, fat signal has confounding T1 effects and the nonzero mean noise in low signal-to-noise ratio (SNR) magnitude images can lead to incorrect estimation of the true lipid percentage. In this study, the effects of bias from T1 effects and image noise were investigated. An oil/water phantom with volume fat-fractions ranging linearly from 0% to 100% was designed and validated using a spoiled gradient echo (SPGR) sequence in combination with a chemical-shift based fat-water separation method known as iterative decomposition of water and fat with echo asymmetry and least squares estimation (IDEAL). We demonstrated two approaches to reduce the effects of T1: small flip angle (flip angle) and dual flip angle methods. Both methods were shown to effectively minimize deviation of the measured fat-fraction from its true value. We also demonstrated two methods to reduce noise bias: magnitude discrimination and phase-constrained reconstruction. Both methods were shown to reduce this noise bias effectively from 15% to less than 1%. Magn Reson Med 58:354–364, 2007. © 2007 Wiley-Liss, Inc.

Acoustic Beamforming for Speaker Diarization of Meetings

Abstract: When performing speaker diarization on recordings from meetings, multiple microphones of different qualities are usually available and distributed around the meeting room. Although several approaches have been proposed in recent years to take advantage of multiple microphones, they are either too computationally expensive and not easily scalable or they cannot outperform the simpler case of using the best single microphone. In this paper, the use of classic acoustic beamforming techniques is proposed together with several novel algorithms to create a complete frontend for speaker diarization in the meeting room domain. New techniques we are presenting include blind reference-channel selection, two-step time delay of arrival (TDOA) Viterbi postprocessing, and a dynamic output signal weighting algorithm, together with using such TDOA values in the diarization to complement the acoustic information. Tests on speaker diarization show a 25% relative improvement on the test set compared to using a single most centrally located microphone. Additional experimental results show improvements using these techniques in a speech recognition task.

Spatiotemporal order out of noise

Abstract: Natural systems are undeniably subject to random fluctuations, arising from either environmental variability or thermal effects. The consideration of those fluctuations supposes to deal with noisy quantities whose variance might at times be a sizable fraction of their mean levels. It is known that, under these conditions, noisy fluctuations can interact with the system's nonlinearities to render counterintuitive behavior, in which an increase in the noise level produces a more regular behavior. In systems with spatial degrees of freedom, this regularity takes the form of spatiotemporal order. An overview is presented of the mechanisms through which noise induces, enhances, and sustains ordered behavior in passive and active nonlinear media, and different spatiotemporal phenomena are described resulting from these effects. The general theoretical framework used in the analysis of these effects is reviewed, encompassing the theory of stochastic partial differential equations and coupled sets of ordinary stochastic differential equations. Experimental observations of self-organized behavior arising out of noise are also described, and details on the numerical algorithms needed in the computer simulation of these problems are given.

Noise Pollution: A Modern Plague

Abstract: Noise is defined as unwanted sound. Environmental noise consists of all the unwanted sounds in our communities except that which originates in the workplace. Environmental noise pollution, a form of air pollution, is a threat to health and well-being. It is more severe and widespread than ever before, and it will continue to increase in magnitude and severity because of population growth, urbanization, and the associated growth in the use of increasingly powerful, varied, and highly mobile sources of noise. It will also continue to grow because of sustained growth in highway, rail, and air traffic, which remain major sources of environmental noise. The potential health effects of noise pollution are numerous, pervasive, persistent, and medically and socially significant. Noise produces direct and cumulative adverse effects that impair health and that degrade residential, social, working, and learning environments with corresponding real (economic) and intangible (well-being) losses. It interferes with sleep, concentration, communication, and recreation. The aim of enlightened governmental controls should be to protect citizens from the adverse effects of airborne pollution, including those produced by noise. People have the right to choose the nature of their acoustical environment; it should not be imposed by others.

A Variational Approach to Reconstructing Images Corrupted by Poisson Noise

Abstract: We propose a new variational model to denoise an image corrupted by Poisson noise. Like the ROF model described in [1] and [2], the new model uses total-variation regularization, which preserves edges. Unlike the ROF model, our model uses a data-fidelity term that is suitable for Poisson noise. The result is that the strength of the regularization is signal dependent, precisely like Poisson noise. Noise of varying scales will be removed by our model, while preserving low-contrast features in regions of low intensity.

Oriented Speckle Reducing Anisotropic Diffusion

Abstract: Ultrasound imaging systems provide the clinician with noninvasive, low-cost, and real-time images that can help them in diagnosis, planning, and therapy. However, although the human eye is able to derive the meaningful information from these images, automatic processing is very difficult due to noise and artifacts present in the image. The speckle reducing anisotropic diffusion filter was recently proposed to adapt the anisotropic diffusion filter to the characteristics of the speckle noise present in the ultrasound images and to facilitate automatic processing of images. We analyze the properties of the numerical scheme associated with this filter, using a semi-explicit scheme. We then extend the filter to a matrix anisotropic diffusion, allowing different levels of filtering across the image contours and in the principal curvature directions. We also show a relation between the local directional variance of the image intensity and the local geometry of the image, which can justify the choice of the gradient and the principal curvature directions as a basis for the diffusion matrix. Finally, different filtering techniques are compared on a 2-D synthetic image with two different levels of multiplicative noise and on a 3-D synthetic image of a Y-junction, and the new filter is applied on a 3-D real ultrasound image of the liver

Most likely heteroscedastic Gaussian process regression

Abstract: This paper presents a novel Gaussian process (GP) approach to regression with input-dependent noise rates. We follow Goldberg et al.'s approach and model the noise variance using a second GP in addition to the GP governing the noise-free output value. In contrast to Goldberg et al., however, we do not use a Markov chain Monte Carlo method to approximate the posterior noise variance but a most likely noise approach. The resulting model is easy to implement and can directly be used in combination with various existing extensions of the standard GPs such as sparse approximations. Extensive experiments on both synthetic and real-world data, including a challenging perception problem in robotics, show the effectiveness of most likely heteroscedastic GP regression.

The impacts of anthropogenic ocean noise on cetaceans and implications for management

Abstract: Ocean noise pollution is of special concern for cetaceans, as they are highly dependent on sound as their principal sense. Sound travels very efficiently underwater, so the potential area impacted ...

State-dependent diffusion: Thermodynamic consistency and its path integral formulation

Abstract: The friction coefficient of a particle can depend on its position, as it does when the particle is near a wall. We formulate the dynamics of particles with such state-dependent friction coefficients in terms of a general Langevin equation with multiplicative noise, whose evaluation requires the introduction of specific rules. Two common conventions, the Ito and the Stratonovich, provide alternative rules for evaluation of the noise, but other conventions are possible. We show that the requirement that a particle's distribution function approach the Boltzmann distribution at long times dictates that a drift term must be added to the Langevin equation. This drift term is proportional to the derivative of the diffusion coefficient times a factor that depends on the convention used to define the multiplicative noise. We explore the consequences of this result in a number of examples with spatially varying diffusion coefficients. We also derive a path integral representation for arbitrary interpretation of the noise, and use it in a perturbative study of correlations in a simple system.

Handbook of Noise and Vibration Control

Abstract: This article reviews Handbook of Noise and Vibration Control by Malcolm J. Crocker , New Jersey, 2007 1584 pp. Price: $195.00 (hardcover) ISBN: 0471395994

Wind turbine noise, annoyance and self-reported health and well-being in different living environments

Abstract: Aims The aims of this thesis were to describe and gain an understanding of how people who live in the vicinity of wind turbines are affected by wind turbine noise, and how individual, situational and visual factors, as well as sound properties, moderate the response. Methods A cross-sectional study was carried out in a flat, mainly rural area in Sweden, with the objective to estimate the prevalence of noise annoyance and to examine the dose-response relationship between Aweighted sound pressure levels (SPLs) and perception of and annoyance with wind turbine noise. Subjective responses were obtained through a questionnaire (n = 513; response rate: 68%) and outdoor, Aweighted SPLs were calculated for each respondent. To gain a deeper understanding of the observed noise annoyance, 15 people living in an area were interviewed using open-ended questions. The interviews were analysed using the comparative method of Grounded Theory (GT). An additional cross-sectional study, mainly exploring the influence of individual and situational factors, was carried out in seven areas in Sweden that differed with regard to terrain (flat or complex) and degree of urbanization (n = 765; response rate: 58%). To further explore the impact of visual factors, data from the two cross-sectional studies were tested with structural equation modelling. A proposed model of the influence of visual attitude on noise annoyance, also comprising the influence of noise level and general attitude, was tested among respondents who could see wind turbines versus respondents who could not see wind turbines from their dwelling, and respondents living in flat versus complex terrain. Results Dose-response relationships were found both for perception of noise and for noise annoyance in relation to A-weighted SPLs. The risk of annoyance was enhanced among respondents who could see at least one turbine from their dwelling and among those living in a rural in comparison with a suburban area. Noise from wind turbines was appraised as an intrusion of privacy among people who expected quiet and peace in their living environment. Negative experiences that led to feelings of inferiority added to the distress. Sound characteristics describing the amplitude modulated aerodynamic sound were appraised as the most annoying (swishing, whistling and pulsating/throbbing). Wind turbines were judged as environmentally friendly, efficient and necessary, but also as ugly and unnatural. Being negative towards the visual impact of the wind turbines on the landscape scenery, rather than towards wind turbines as such, was strongly associated with annoyance. Self-reported health impairment was not correlated to SPL, while decreased well-being was associated with noise annoyance. Indications of possible hindrance to psychophysiological restoration were observed. Conclusions Wind turbine noise is easily perceived and is annoying even at low A-weighted SPLs. This could be due to perceived incongruence between the characteristics of wind turbine noise and the background sound. Wind turbines are furthermore prominent objects whose rotational movement attracts the eye. Multimodal sensory effects or negative aesthetic response could enhance the risk of noise annoyance. Adverse reactions could possibly lead to stress-related symptoms due to prolonged physiological arousal and hindrance to psychophysiological restoration. The observed differences in prevalence of noise annoyance between living environments make it necessary to assess separate doseresponse relationships for different types of landscapes.

Improving Adaptive Kalman Estimation in GPS/INS Integration

Abstract: The central task of GPS/INS integration is to effectively blend GPS and INS data together to generate an optimal solution. The present data fusion algorithms, which are mostly based on Kalman filtering (KF), have several limitations. One of those limitations is the stringent requirement on precise a priori knowledge of the system models and noise properties. Uncertainty in the covariance parameters of the process noise (Q) and the observation errors (R) may significantly degrade the filtering performance. The conventional way of determining Q and R relies on intensive analysis of empirical data. However, the noise levels may change in different applications. Over the past few decades adaptive KF algorithms have been intensively investigated with a view to reducing the influence of the Q and R definition errors. The covariance matching method has been shown to be one of the most promising techniques. This paper first investigates the utilization of an online stochastic modelling algorithm with regards to its parameter estimation stability, convergence, optimal window size, and the interaction between Q and R estimations. Then a new adaptive process noise scaling algorithm is proposed. Without artificial or empirical parameters being used, the proposed adaptive mechanism has demonstrated the capability of autonomously tuning the process noise covariance to the optimal magnitude, and hence improving the overall filtering performance.

Robust Speaker Recognition in Noisy Conditions

Abstract: This paper investigates the problem of speaker identification and verification in noisy conditions, assuming that speech signals are corrupted by environmental noise, but knowledge about the noise characteristics is not available. This research is motivated in part by the potential application of speaker recognition technologies on handheld devices or the Internet. While the technologies promise an additional biometric layer of security to protect the user, the practical implementation of such systems faces many challenges. One of these is environmental noise. Due to the mobile nature of such systems, the noise sources can be highly time-varying and potentially unknown. This raises the requirement for noise robustness in the absence of information about the noise. This paper describes a method that combines multicondition model training and missing-feature theory to model noise with unknown temporal-spectral characteristics. Multicondition training is conducted using simulated noisy data with limited noise variation, providing a ldquocoarserdquo compensation for the noise, and missing-feature theory is applied to refine the compensation by ignoring noise variation outside the given training conditions, thereby reducing the training and testing mismatch. This paper is focused on several issues relating to the implementation of the new model for real-world applications. These include the generation of multicondition training data to model noisy speech, the combination of different training data to optimize the recognition performance, and the reduction of the model's complexity. The new algorithm was tested using two databases with simulated and realistic noisy speech data. The first database is a redevelopment of the TIMIT database by rerecording the data in the presence of various noise types, used to test the model for speaker identification with a focus on the varieties of noise. The second database is a handheld-device database collected in realistic noisy conditions, used to further validate the model for real-world speaker verification. The new model is compared to baseline systems and is found to achieve lower error rates.

Evaluation of Spectrum Occupancy in Indoor and Outdoor Scenario in the Context of Cognitive Radio

Abstract: Dynamic spectrum access is an integral part of the Cognitive Radio paradigm. However, efficient spectrum sensing techniques are crucial on the way towards systems, which use idle spectrum bands opportunistically and increase the overall spectral efficiency. Current spectrum occupancy was evaluated in few measurement campaigns at different locations mostly located in the USA. In this paper we report about an extensive measurement campaign conducted in Aachen, Germany, comparing indoor-and outdoor measurement results. The highly sensitive measurement system enabled us to measure also man-made or ambient noise. Since an energy detector cannot differentiate such noise from other primary user signals we determine a very high spectrum occupancy in the outdoor scenario in the band from 20 MHz up to 3 GHz. Considerably less occupation was measured in the indoor scenario also because of less ambient noise. Our measurements confirm that the spectrum band 3-6 GHz is rarely occupied. We further provide a case study how the amplitude probability distribution can be used together with detailed regulatory information to infer additional information about the spectral usage. Such information is beneficial in order to optimize the spectrum sensing process and identify candidate bands for further investigation and possible secondary usage.

A Detection Statistic for Random-Valued Impulse Noise

Abstract: This paper proposes an image statistic for detecting random-valued impulse noise. By this statistic, we can identify most of the noisy pixels in the corrupted images. Combining it with an edge-preserving regularization, we obtain a powerful two-stage method for denoising random-valued impulse noise, even for noise levels as high as 60%. Simulation results show that our method is significantly better than a number of existing techniques in terms of image restoration and noise detection

Clinical DT-MRI Estimation, Smoothing, and Fiber Tracking With Log-Euclidean Metrics

Abstract: Diffusion tensor magnetic resonance imaging (DT-MRI or DTI) is an imaging modality that is gaining importance in clinical applications. However, in a clinical environment, data have to be acquired rapidly, often at the expense of the image quality. This often results in DTI datasets that are not suitable for complex postprocessing like fiber tracking. We propose a new variational framework to improve the estimation of DT-MRI in this clinical context. Most of the existing estimation methods rely on a log-Gaussian noise (Gaussian noise on the image logarithms), or a Gaussian noise, that do not reflect the Rician nature of the noise in MR images with a low signal-to-noise ratio (SNR). With these methods, the Rician noise induces a shrinking effect: the tensor volume is underestimated when other noise models are used for the estimation. In this paper, we propose a maximum likelihood strategy that fully exploits the assumption of a Rician noise. To further reduce the influence of the noise, we optimally exploit the spatial correlation by coupling the estimation with an anisotropic prior previously proposed on the spatial regularity of the tensor field itself, which results in a maximum a posteriori estimation. Optimizing such a nonlinear criterion requires adapted tools for tensor computing. We show that Riemannian metrics for tensors, and more specifically the log-Euclidean metrics, are a good candidate and that this criterion can be efficiently optimized. Experiments on synthetic data show that our method correctly handles the shrinking effect even with very low SNR, and that the positive definiteness of tensors is always ensured. Results on real clinical data demonstrate the truthfulness of the proposed approach and show promising improvements of fiber tracking in the brain and the spinal cord.

Fast rates for support vector machines using Gaussian kernels

Abstract: For binary classification we establish learning rates up to the order of $n^{-1}$ for support vector machines (SVMs) with hinge loss and Gaussian RBF kernels. These rates are in terms of two assumptions on the considered distributions: Tsybakov's noise assumption to establish a small estimation error, and a new geometric noise condition which is used to bound the approximation error. Unlike previously proposed concepts for bounding the approximation error, the geometric noise assumption does not employ any smoothness assumption.

Short-and long-term changes in right whale calling behavior : The potential effects of noise on acoustic communication

Abstract: The impact of anthropogenic noise on marine mammals has been an area of increasing concern over the past two decades. Most low-frequency anthropogenic noise in the ocean comes from commercial shipping which has contributed to an increase in ocean background noise over the past 150 years. The long-term impacts of these changes on marine mammals are not well understood. This paper describes both short- and long-term behavioral changes in calls produced by the endangered North Atlantic right whale (Eubalaena glacialis) and South Atlantic right whale (Eubalaena australis) in the presence of increased low-frequency noise. Right whales produce calls with a higher average fundamental frequency and they call at a lower rate in high noise conditions, possibly in response to masking from low-frequency noise. The long-term changes have occurred within the known lifespan of individual whales, indicating that a behavioral change, rather than selective pressure, has resulted in the observed differences. This study provides evidence of a behavioral change in sound production of right whales that is correlated with increased noise levels and indicates that right whales may shift call frequency to compensate for increased band-limited background noise.

Blind Acoustic Beamforming Based on Generalized Eigenvalue Decomposition

Abstract: Maximizing the output signal-to-noise ratio (SNR) of a sensor array in the presence of spatially colored noise leads to a generalized eigenvalue problem. While this approach has extensively been employed in narrowband (antenna) array beamforming, it is typically not used for broadband (microphone) array beamforming due to the uncontrolled amount of speech distortion introduced by a narrowband SNR criterion. In this paper, we show how the distortion of the desired signal can be controlled by a single-channel post-filter, resulting in a performance comparable to the generalized minimum variance distortionless response beamformer, where arbitrary transfer functions relate the source and the microphones. Results are given both for directional and diffuse noise. A novel gradient ascent adaptation algorithm is presented, and its good convergence properties are experimentally revealed by comparison with alternatives from the literature. A key feature of the proposed beamformer is that it operates blindly, i.e., it neither requires knowledge about the array geometry nor an explicit estimation of the transfer functions from source to sensors or the direction-of-arrival.

Intelligent Neural Network-Based Fast Power System Harmonic Detection

Abstract: Nowadays, harmonic distortion in power systems is attracting significant attention. Traditional technical tools for harmonic distortion analysis using either fast Fourier transform or discrete Fourier transform are, however, susceptible to the presence of noise in the distorted signals. Harmonic detection by using Fourier transformation also requires input data for more than one cycle of the current waveform and requires time for the analysis in the next coming cycle. In this paper, an alternative method using neural network algorithm has achieved satisfactory results for fast and precise harmonic detection in noisy environments by providing only 1/2 cycle sampled values of distorted waveforms to neural network. Sensitivity considerations are conducted to determine the key factors affecting the performance efficiency of the proposed model to reach the lowest errors of testing patterns

Improving wireless simulation through noise modeling

Abstract: We propose modeling environmental noise in order to efficiently and accurately simulate wireless packet delivery. We measure noise traces in many different environments and propose three algorithms to simulate noise from these traces. We evaluate applying these algorithms to signal-to-noise curves in comparison to existing simulation approaches used in EmStar, TOSSIM, and ns2. We measure simulation accuracy using the Kantorovich-Wasserstein distance on conditional packet delivery functions. We demonstrate that using a closest-fit pattern matching (CPM) noise model can capture complex temporal dynamics which existing approaches do not, increasing packet simulation fidelity by a factor of 2 for good links, a factor of 1.5 for bad links, and a factor of 5 for intermediate links. As our models are derived from real-world traces, they can be generated for many different environments.

Interactions between Cognition, Compression, and Listening Conditions: Effects on Speech-in-Noise Performance in a Two-Channel Hearing Aid

Abstract: This study which included 23 experienced hearing aid users replicated several of the experiments reported in Gatehouse et al (2003, 2006) with new speech test material, language, and test procedure. The performance measure used was SNR required for 80% correct words in a sentence test. Consistent with Gatehouse et al, this study indicated that subjects showing a low score in a cognitive test (visual letter monitoring) performed better in the speech recognition test with slow time constants than with fast time constants, and performed better in unmodulated noise than in modulated noise, while subjects with high scores on the cognitive test showed the opposite pattern. Furthermore, cognitive test scores were significantly correlated with the differential advantage of fast-acting versus slow-acting compression in conditions of modulated noise. The pure tone average threshold explained 30% of the variance in aided speech recognition in noise under relatively simple listening conditions, while cognitive test scores explained about 40% of the variance under more complex, fluctuating listening conditions, where the pure tone average explained less than 5% of the variance. This suggests that speech recognition under steady-state noise conditions may underestimate the role of cognition in real-life listening.

Low-Frequency Noise in Advanced MOS Devices

Abstract: Low-Frequency Noise in Advanced CMOS Devices begins with an introduction to noise, describing the fundamental noise sources and basic circuit analysis. The characterization of low-frequency noise is discussed in detail and useful practical advice is given. The various theoretical and compact low-frequency (1/f) noise models in MOS transistors are treated extensively providing an in-depth understanding of the low-frequency noise mechanisms and the potential sources of the noise in MOS transistors. Advanced CMOS technology including nanometer scaled devices, strained Si, SiGe, SOI, high-k gate dielectrics, multiple gates and metal gates are discussed from a low-frequency noise point of view. Some of the most recent publications and conference presentations are included in order to give the very latest view on the topics. The book ends with an introduction to noise in analog/RF circuits and describes how the low-frequency noise can affect these circuits.

Listen to the noise: noise is beneficial for cognitive performance in ADHD

Abstract: Background: Noise is typically conceived of as being detrimental to cognitive performance. However, given the mechanism of stochastic resonance, a certain amount of noise can benefit performance. We investigate cognitive performance in noisy environments in relation to a neurocomputational model of attention deficit hyperactivity disorder (ADHD) and dopamine. The Moderate Brain Arousal model (MBA; Sikstrom & Soderlund, 2007) suggests that dopamine levels modulate how much noise is required for optimal cognitive performance. We experimentally examine how ADHD and control children respond to different encoding conditions, providing different levels of environmental stimulation. Methods: Participants carried out self-performed mini tasks (SPT), as a high memory performance task, and a verbal task (VT), as a low memory task. These tasks were performed in the presence, or absence, of auditory white noise. Results: Noise exerted a positive effect on cognitive performance for the ADHD group and deteriorated performance for the control group, indicating that ADHD subjects need more noise than controls for optimal cognitive performance. Conclusions: The positive effect of white noise is explained by the phenomenon of stochastic resonance (SR), i.e., the phenomenon that moderate noise facilitates cognitive performance. The MBA model suggests that noise in the environment, introduces internal noise into the neural system through the perceptual system. This noise induces SR in the neurotransmitter systems and makes this noise beneficial for cognitive performance. In particular, the peak of the SR curve depends on the dopamine level, so that participants with low dopamine levels (ADHD) require more noise for optimal cognitive performance compared to controls.