Noise Segments Research Articles

MEMS Gyroscope Temperature Compensation Based on Improved Complete Ensemble Empirical Mode Decomposition and Optimized Extreme Learning Machine.

Herein, we investigate the temperature compensation for a dual-mass MEMS gyroscope. After introducing and simulating the dual-mass MEMS gyroscope's working modes, we propose a hybrid algorithm for temperature compensation relying on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sample entropy, time-frequency peak filtering, non-dominated sorting genetic algorithm-II (NSGA II) and extreme learning machine. Firstly, we use ICEEMDAN to decompose the gyroscope's output signal, and then we use sample entropy to classify the decomposed signals. For noise segments and mixed segments with different levels of noise, we use time-frequency peak filtering with different window lengths to achieve a trade-off between noise removal and signal retention. For the feature segment with temperature drift, we build a compensation model using extreme learning machine. To improve the compensation accuracy, NSGA II is used to optimize extreme learning machine, with the prediction error and the 2-norm of the output-layer connection weight as the optimization objectives. Enormous simulation experiments prove the excellent performance of our proposed scheme, which can achieve trade-offs in signal decomposition, classification, denoising and compensation. The improvement in the compensated gyroscope's output signal is analyzed based on Allen variance; its angle random walk is decreased from 0.531076°/h/√Hz to 6.65894 × 10-3°/h/√Hz and its bias stability is decreased from 32.7364°/h to 0.259247°/h.

A Temperature Compensation Approach for Micro-Electro-Mechanical Systems Accelerometer Based on Gated Recurrent Unit-Attention and Robust Local Mean Decomposition-Sample Entropy-Time-Frequency Peak Filtering.

MEMS accelerometers are significantly impacted by temperature and noise, leading to a considerable compromise in their accuracy. In response to this challenge, we propose a parallel denoising and temperature compensation fusion algorithm for MEMS accelerometers based on RLMD-SE-TFPF and GRU-attention. Firstly, we utilize robust local mean decomposition (RLMD) to decompose the output signal of the accelerometer into a series of product function (PF) signals and a residual signal. Secondly, we employ sample entropy (SE) to classify the decomposed signals, categorizing them into noise segments, mixed segments, and temperature drift segments. Next, we utilize the time-frequency peak filtering (TFPF) algorithm with varying window lengths to separately denoise the noise and mixed signal segments, enabling subsequent signal reconstruction and training. Considering the strong inertia of the temperature signal, we innovatively introduce the accelerometer's output time series as the model input when training the temperature compensation model. We incorporate gated recurrent unit (GRU) and attention modules, proposing a novel GRU-MLP-attention model (GMAN) architecture. Simulation experiments demonstrate the effectiveness of our proposed fusion algorithm. After processing the accelerometer output signal through the RLMD-SE-TFPF denoising algorithm and the GMAN temperature drift compensation model, the acceleration random walk is reduced by 96.11%, with values of 0.23032 g/h/Hz for the original accelerometer output signal and 0.00895695 g/h/Hz for the processed signal.

Effects of entropy in real-world noise on speech perception in listeners with normal hearing and hearing lossa).

Hearing aids show more benefit in traditional laboratory speech-in-noise tests than in real-world noisy environments. Real-world noise comprises a large range of acoustic properties that vary randomly and rapidly between and within environments, making quantifying real-world noise and using it in experiments and clinical tests challenging. One approach is to use acoustic features and statistics to quantify acoustic properties of real-world noise and control for them or measure their relationship to listening performance. In this study, the complexity of real-world noise from different environments was quantified using entropy in both the time- and frequency-domains. A distribution of noise segments from low to high entropy were extracted. Using a trial-by-trial design, listeners with normal hearing and hearing loss (in aided and unaided conditions) repeated back sentences embedded in these noise segments. Entropy significantly affected speech perception, with a larger effect of entropy in the time-domain than the frequency-domain, a larger effect for listeners with normal hearing than for listeners with hearing loss, and a larger effect for listeners with hearing loss in the aided than unaided condition. Speech perception also differed between most environment types. Combining entropy with the environment type improved predictions of speech perception above the environment type alone.

Snapping shrimp noise detection methods based on linear prediction analysis

This paper proposes features for detecting snapping shrimp noise based on linear predictive analysis. Snapping shrimps are a species inhabiting the ocean depths and are one of the main sources of underwater noise due to their high-amplitude signals that occur frequently. The proposed features utilize the characteristic of sudden onset and rapid decay of pistol shrimp noise by using linear predictive analysis to accurately detect the noise segments and mitigate the impact of snapping shrimp noise. The large error between the predicted values by linear predictive analysis and the actual measured values allows for effective detection of pistol shrimp segments. Additionally, the proposed features are combined with a fixed-threshold detector to further improve the performance of noise segment detection. When compared to the state-of-the-art method known as multilayer wavelet packet decomposition, the proposed method outperformed with an average improvement of 0.12 in terms of receiver operating characteristic curve and area under the curve. Moreover, the proposed method had lower computational complexity.

DESIGN OF HYBRID ADAPTIVE MODEL FOR IMAGE DENOISING

This thesis work proposes a new denoising algorithm based on Particle filter and Wavelet (Curvelet) transform combination, particle filter generates weights through SIR algorithm to cancel the interference of noise present in the image, while curvelet transform is used to shrink the remaining segments of noise, so this method can both remove image blurr and maintain good texture as well. The PF+Clet Image Denoiser is successfully designed and implemented, which is a new approach in image enhancement and Interference cancellation. This thesis concludes that it is quite efficient algorithm among other adaptive filtering techniques. Experimental results also show that proposed algorithm performs extremely well when noise density is increased, as obtained image is completely visible. This approach comprises of generation of particles by performing weight normalization, resampling and update state. Therefore, for large number of particles execution time is more when compared to other adaptive filtering approach. Key Words: Particle Filter(PF), Curvelet(CLet), Wavelet(Wlet), Peak signal to noise Ratio(PSNR), Deblurring.

An Investigation of ECAPA-TDNN Audio Type Recognition Method Based on Mel Acoustic Spectrograms

Audio signals play a crucial role in our perception of our surroundings. People rely on sound to assess motion, distance, direction, and environmental conditions, aiding in danger avoidance and decision making. However, in real-world environments, during the acquisition and transmission of audio signals, we often encounter various types of noises that interfere with the intended signals. As a result, the essential features of audio signals become significantly obscured. Under the interference of strong noise, identifying noise segments or sound segments, and distinguishing audio types becomes pivotal for detecting specific events and sound patterns or isolating abnormal sounds. This study analyzes the characteristics of Mel’s acoustic spectrogram, explores the application of the deep learning ECAPA-TDNN method for audio type recognition, and substantiates its effectiveness through experiments. Ultimately, the experimental results demonstrate that the deep learning ECAPA-TDNN method for audio type recognition, utilizing Mel’s acoustic spectrogram as features, achieves a notably high recognition accuracy.

A Segmentation-Denoising Network for Artifact Removal From Single-Channel EEG

As an important neurorecording technique, electroencephalography (EEG) is often contaminated by various artifacts, which obstructs subsequent analysis. In recent years, deep learning-based methods have been proven to be promising for artifact removal. However, most denoising methods focus on recovering clean EEG from raw signals contaminated by the noise over the entire recording period, ignoring that the practical EEG recordings may contain clean segments in addition to noise segments. Therefore, the general model may cause distortion when dealing with clean segments. In this paper, we propose a simple, yet effective segmentation-denoising network (SDNet) for artifact removal. The proposed method is capable of differentiating noisy EEG segments from clean ones via semantic segmentation, avoiding the distortion caused by processing clean segments. We conduct a performance comparison on semi-simulated and real EEG data. The experimental results demonstrate that SDNet outperforms the state-of-art approaches. This work provides a novel way to reconstruct artifact-attenuated EEG signals, and may further benefit the EEG-based diagnosis and treatment.

Neurophysiological time course of timbre-induced music-like perception.

Traditionally, pitch variation in a sound stream has been integral to music identity. We attempt to expand music's definition, by demonstrating that the neural code for musicality is independent of pitch encoding. That is, pitchless sound streams can still induce music-like perception and a neurophysiological hierarchy similar to pitched melodies. Previous work reported that neural processing of sounds with no-pitch, fixed-pitch, and irregular-pitch (melodic) patterns, exhibits a right-lateralized hierarchical shift, with pitchless sounds favorably processed in Heschl's gyrus (HG), ascending laterally to nonprimary auditory areas for fixed-pitch and even more laterally for melodic patterns. The objective of this EEG study was to assess whether sound encoding maintains a similar hierarchical profile when musical perception is driven by timbre irregularities in the absence of pitch changes. Individuals listened to repetitions of three musical and three nonmusical sound-streams. The nonmusical streams were comprised of seven 200-ms segments of white, pink, or brown noise, separated by silent gaps. Musical streams were created similarly, but with all three noise types combined in a unique order within each stream to induce timbre variations and music-like perception. Subjects classified the sound streams as musical or nonmusical. Musical processing exhibited right dominant α power enhancement, followed by a lateralized increase in θ phase-locking and spectral power. The θ phase-locking was stronger in musicians than in nonmusicians. The lateralization of activity suggests higher-level auditory processing. Our findings validate the existence of a hierarchical shift, traditionally observed with pitched-melodic perception, underscoring that musicality can be achieved with timbre irregularities alone.NEW & NOTEWORTHY EEG induced by streams of pitchless noise segments varying in timbre were classified as music-like and exhibited a right-lateralized hierarchy in processing similar to pitched melodic processing. This study provides evidence that the neural-code of musicality is independent of pitch encoding. The results have implications for understanding music processing in individuals with degraded pitch perception, such as in cochlear-implant listeners, as well as the role of nonpitched sounds in the induction of music-like perceptual states.

Effects of age on brainstem coding of speech glimpses in interrupted noise

Difficulty understanding speech in fluctuating backgrounds is common among older adults. Whereas younger adults are adept at interpreting speech based on brief moments when the signal-to-noise ratio is favorable, older adults use these glimpses of speech less effectively. Age-related declines in auditory brainstem function may degrade the fidelity of speech cues in fluctuating noise for older adults, such that brief glimpses of speech interrupted by noise segments are not faithfully represented in the neural code that reaches the cortex. This hypothesis was tested using electrophysiological recordings of the envelope following response (EFR) elicited by glimpses of speech-like stimuli varying in duration (42, 70, 210 ms) and interrupted by silence or intervening noise. Responses from adults aged 23–73 years indicated that both age and hearing sensitivity were associated with EFR temporal coherence and response magnitude. Age was better than hearing sensitivity for predicting temporal coherence, whereas hearing sensitivity was better than age for predicting response magnitude. Poorer-fidelity EFRs were observed with shorter glimpses and with the addition of intervening noise. However, losses of fidelity with glimpse duration and noise were not associated with participant age or hearing sensitivity. These results suggest that the EFR is sensitive to factors commonly associated with glimpsing but do not entirely account for age-related changes in speech recognition in fluctuating backgrounds.

Characteristics of Alveolo-palatal Affricates Produced by Mandarin-speaking Children with Repaired Cleft Palate.

Objectives: In this study, examined the acoustic properties of affricates /t/ and /th/ in Mandarin Chinese, and analyzed the differences of the acoustic characteristics of these affricates produced by children with repaired cleft palate and normally developing children. We also explored the relationship between the affricates and high-front vowel /i/. Methods: We analyzed 16 monosyllabic words with alveolo-palatal affricates as the initial consonants produced by children with repaired cleft palate (N=13, Mean=5.9 years) and normally developing children (N=6, Mean age=5.3 years). We used several acoustic parameters to investigate the characteristics of these affricates, such as the center of gravity, VOT and the formants of vowels. Results: Compared with normally developing children, children with cleft palate exhibited a lower center of gravity for the 2 affricates /t/ and /th/. Data from the control group showed that the affricate /th/ had a significantly greater center of gravity than that of /t/. The accuracy of /t , th/ produced by speakers of cleft palate was significantly correlated with that of /i/ (r=0.63). High-front vowel /i/ is a significant index in diagnosing speech intelligibility which is more valuable than /a/ and /u/. There was a significant difference in F2 of vowel /i/ between children with cleft palate without speech therapy (CS1) and after speech therapy (CS2). After speech intervention, the accuracy of affricates produced by children with cleft palate was improved, the acoustic properties "stop + noise segments" appeared. Conclusion: Children with cleft palate can be distinguished better from children with normal development by 2 significant acoustic characteristics: center of gravity and VOT. As alveolo-palatal affricates /t , th/ and high-front vowel /i/ have a similar place of articulation, front-tongue-blade, their production accuracy can be improved mutually. The analysis showed that the articulation of Chinese /i/ has a higher frontal lingual position and less variability, which is more conducive to articulation training and improves the effect of cleft palate training. These findings provide a potential relationship on affricates /t, th/ and vowel /i/. Children with cleft palate have difficulty pronouncing the /t, t h/ and /i/. It is better to start with a vowel /i/, resulting in improvement in overall speech intelligibility.

Within- and between-subject consistency of perceptual segmentation in periodic noise: A combined behavioral tapping and EEG study.

It is remarkable that human listeners can perceive periodicity in noise, as the isochronous repetition of a particular noise segment is not accompanied by salient physical cues in the acoustic signal. Previous research suggested that listeners rely on short temporally local and idiosyncratic features to perceptually segment periodic noise sequences. The present study sought to test this assumption by disentangling consistency of perceptual segmentation within and between listeners. Presented periodic noise sequences either consisted of seamless repetitions of a 500-ms segment or of repetitions of a 200-ms segment that were interleaved with 300-ms portions of random noise. Both within- and between-subject consistency was stronger for interleaved (compared with seamless) periodic sequences. The increased consistency likely resulted from reduced temporal jitter of potential features used for perceptual segmentation when the recurring segment was shorter and occurred interleaved with random noise. These results support the notion that perceptual segmentation of periodic noise relies on subtle temporally local features. However, the finding that some specific noise sequences were segmented more consistently across listeners than others challenges the assumption that the features are necessarily idiosyncratic. Instead, in some specific noise samples, a preference for certain spectral features is shared between individuals.

Analysis of Ringdown Overtones in GW150914.

We analyze GW150914 postmerger data to understand if ringdown overtone detection claims are robust. We find no evidence in favor of an overtone in the data after the waveform peak. Around the peak, the Bayes factor does not indicate the presence of an overtone, while the support for a nonzero amplitude is sensitive to changes in the starting time much smaller than the overtone damping time. This suggests that claims of an overtone detection are noise dominated. We perform GW150914-like injections in neighboring segments of the real detector noise, and we show that noise can indeed induce artificial evidence for an overtone.

Re-weighted zero attracting adaptive exponential FLAF with maximum correntropy criterion for robust sparse nonlinear system identification

The profoundly employed linear-in-the-parameter nonlinear adaptive filtering techniques of functional link adaptive filter (FLAF) and adaptive exponential FLAF (AEFLAF) often exhibit deteriorated performances in the wake of varying sparsity conditions and impulsive noise interferences. In this regard, a robust nonlinear adaptive filtering technique is introduced leveraging the benefits of nonlinear similarity index maximum correntropy criterion (MCC). Additionally a log sum penalty function is included in the derived cost function in response for sparse system identification. The entire robust nonlinear adaptive filter in this paper is constructed upon the framework of AEFLAF based on affine projection algorithm (APA) as MCC based reweighted zero attracting AEFLAF (MR-AEFLAF). Besides, steady state analysis of the proposed MR-AEFLAF is carried out establishing the condition for stability. Simulations with white, colored noise and speech segment illustrate the efficacy of the proposed technique for robust nonlinear adaptive filtering in system identification and acoustic feedback cancellation in hearing aids scenario. The performance of the designed adaptive filter is assessed quantitatively and qualitatively both.

Association Between Operating Room Noise and Team Cognitive Workload in Cardiac Surgery.

Excessive intra-operative noise in cardiac surgery has the potential to serve as source of distraction and additional cognitive workload for the surgical team, and may interfere with optimal performance. The separation from bypass phase is a technically complex phase of surgery, making it highly susceptible to communication breakdowns due to high cognitive demands and requiring tightly coupled team coordination. The objective of this study was to investigate team cognitive workload levels and communication in relation to intra-operative time periods representative of infrequent vs. frequent peaks in ambient noise. Compared to 5-minute segments with no peaks in noise at all, segments with the highest percentage of noise peaks (≥10%) were significantly associated with higher team members' heart rate before, during, and after noise segments analyzed. These noisier segments were also associated with a significantly higher level of case-irrelevant communication events. These data suggest that case-irrelevant conversations associated with a greater degree of excessive peaks in noise may be associated with team workload levels, warranting further investigation into efforts to standardize communication during critical surgical phases.

Software Application toward Accessible Hearing Care Assessment: Gap in Noise Test.

Currently, several methods are being applied to assess auditory temporal resolution in a controlled clinical environment via the measurements of gap detection thresholds (GDTs). However, these methods face two issues: the relatively long time required to perform the gap detection test in such settings and the potential of inaccessibility to such facilities. This article proposes a fast, affordable, and reliable application-based method for the determination of GDT either inside or outside the soundproof booth. The proposed test and the acoustic stimuli were both developed using the MATLAB® programming platform. GDT is determined when the subject is able to distinguish the shortest silent gap inserted randomly in one of two segments of white noise. GDTs were obtained from 42 normal-hearing subjects inside and outside the soundproof booth. The results of this study indicated that average GDTs measured inside the booth (5.12 ± 1.02 ms) and outside (4.78 ± 1.16 ms) were not significantly different. The measured GDTs were also comparable to that reported in the literature. In addition, the GDT screening time of the proposed method was approximately 5 minutes, a screening time that is much less than that reported by the literature. Data show that the proposed application was fast and reliable to screen GDT compared to the standard method currently used in clinical settings.

Dispersion measurement from ultra-short ambient noise using phase-weighted stacking in urban areas

Passive surface wave methods that utilize ambient noise to investigate underground space have been widely used in densely populated urban areas. As an important step in imaging from ambient noise, dispersion measurement seriously affects the accuracy of the final inversion results. Linear stacking, the most commonly used stacking method in dispersion measurement, usually relies on the long observation time to obtain Green's functions with high signal-to-noise. However, it is sometimes difficult to ensure sufficient observation time for passive surface wave methods for exploration efficiency and environmental restrictions in urban areas, making dispersion measurement from ultra-short noise important and necessary. Considering the limitations of linear stacking, we compared the dispersion images obtained by linear stacking, phase-weighted stacking (PWS), and time-frequency phase-weighted stacking using ultra-short ambient noise to find an appropriate stacking way. Synthetic experiments show that PWS can obtain better cross-correlation functions and dispersion spectra with fewer noise segments. Two field cases further verify the reliability of applying PWS in dispersion measurement from several minutes of the continuous noise record by analyzing the phase velocities error or comparing the inversion results with the drilling data. Both synthetic experiments and field examples illustrate PWS can be used as an efficient and economical method to meet the needs of ultra-short-time imaging and real-time monitoring of urban underground structures.

Temporal loudness weights: Primacy effects, loudness dominance and their interaction.

Loudness judgments of sounds varying in level across time show a non-uniform temporal weighting, with increased weights assigned to the beginning of the sound (primacy effect). In addition, higher weights are observed for temporal components that are higher in level than the remaining components (loudness dominance). In three experiments, sounds consisting of 100- or 475-ms Gaussian wideband noise segments with random level variations were presented and either none, the first, or a central temporal segment was amplified or attenuated. In Experiment 1, the sounds consisted of four 100-ms segments that were separated by 500-ms gaps. Previous experiments did not show a primacy effect in such a condition. In Experiment 2, four- or ten-100-ms-segment sounds without gaps between the segments were presented to examine the interaction between the primacy effect and level dominance. As expected, for the sounds with segments separated by gaps, no primacy effect was observed, but weights on amplified segments were increased and weights on attenuated segments were decreased. For the sounds with contiguous segments, a primacy effect as well as effects of relative level (similar to those in Experiment 1) were found. For attenuation, the data indicated no substantial interaction between the primacy effect and loudness dominance, whereas for amplification an interaction was present. In Experiment 3, sounds consisting of either four contiguous 100-ms or 475-ms segments, or four 100-ms segments separated by 500-ms gaps were presented. Effects of relative level were more pronounced for the contiguous sounds. Across all three experiments, the effects of relative level were more pronounced for attenuation. In addition, the effects of relative level showed a dependence on the position of the change in level, with opposite direction for attenuation compared to amplification. Some of the results are in accordance with explanations based on masking effects on auditory intensity resolution.

Cortical Processing of Binaural Cues as Shown by EEG Responses to Random-Chord Stereograms

Spatial hearing facilitates the perceptual organization of complex soundscapes into accurate mental representations of sound sources in the environment. Yet, the role of binaural cues in auditory scene analysis (ASA) has received relatively little attention in recent neuroscientific studies employing novel, spectro-temporally complex stimuli. This may be because a stimulation paradigm that provides binaurally derived grouping cues of sufficient spectro-temporal complexity has not yet been established for neuroscientific ASA experiments. Random-chord stereograms (RCS) are a class of auditory stimuli that exploit spectro-temporal variations in the interaural envelope correlation of noise-like sounds with interaurally coherent fine structure; they evoke salient auditory percepts that emerge only under binaural listening. Here, our aim was to assess the usability of the RCS paradigm for indexing binaural processing in the human brain. To this end, we recorded EEG responses to RCS stimuli from 12 normal-hearing subjects. The stimuli consisted of an initial 3-s noise segment with interaurally uncorrelated envelopes, followed by another 3-s segment, where envelope correlation was modulated periodically according to the RCS paradigm. Modulations were applied either across the entire stimulus bandwidth (wideband stimuli) or in temporally shifting frequency bands (ripple stimulus). Event-related potentials and inter-trial phase coherence analyses of the EEG responses showed that the introduction of the 3- or 5-Hz wideband modulations produced a prominent change-onset complex and ongoing synchronized responses to the RCS modulations. In contrast, the ripple stimulus elicited a change-onset response but no response to ongoing RCS modulation. Frequency-domain analyses revealed increased spectral power at the fundamental frequency and the first harmonic of wideband RCS modulations. RCS stimulation yields robust EEG measures of binaurally driven auditory reorganization and has potential to provide a flexible stimulation paradigm suitable for isolating binaural effects in ASA experiments.

A Novel Parallel Processing Model for Noise Reduction and Temperature Compensation of MEMS Gyroscope.

To eliminate the noise and temperature drift in an Micro-Electro-Mechanical Systems (MEMS) gyroscope’s output signal for improving measurement accuracy, a parallel processing model based on Multi-objective particle swarm optimization based on variational modal decomposition-time-frequency peak filter (MOVMD–TFPF) and Beetle antennae search algorithm- Elman neural network (BAS–Elman NN) is established. Firstly, variational mode decomposition (VMD) is optimized by multi-objective particle swarm optimization (MOPSO); then, the best decomposition parameters [kbest,abest] can be obtained. Secondly, the gyroscope output signals are decomposed by VMD optimized by MOPSO (MOVMD); then, the intrinsic mode functions (IMFs) obtained after decomposition are classified into a noise segment, mixed segment, and drift segment by sample entropy (SE). According to the idea of a parallel model, the noise segment can be discarded directly, the mixed segment is denoised by time-frequency peak filtering (TFPF), and the drift segment is compensated at the same time. In the compensation part, the beetle antennae search algorithm (BAS) is adopted to optimize the network parameters of the Elman neural network (Elman NN). Subsequently, the double-input/single-output temperature compensation model based on the BAS-Elman NN is established to compensate the drift segment, and these processed segments are reconstructed to form the final gyroscope output signal. Experimental results demonstrate the superiority of this parallel processing model; the angle random walk of the compensated gyroscope output is decreased from 0.531076 to 5.22502 × 10−3°/h/√Hz, and its bias stability is decreased from 32.7364°/h to 0.140403°/h, respectively.

Effects of entropy in real-world noise on speech perception

Traditional tests of speech-in-noise perception use artificial background noise, leading to results that do not generalize to real-world scenarios. There is a significant need for improved ecological validity in laboratory and clinic speech-in-noise tests, particularly with respect to the types of noise used. However, the acoustic complexity and random nature of real-world noise poses challenges to its use and the interpretation of results. In this study, the complexity of real-world noise from 8 environments was systematically quantified in the time and frequency domains using entropy. Then, segments of noise distributed in entropy from low to high were extracted and combined with target sentences at fixed signal-to-noise ratios. Ten normal-hearing subjects were tested on 200 sentences-in-noise across the entropy distribution in both the time and frequency domains in a trial-by-trial design. Linear mixed effect models showed a significant effect of entropy in the time and frequency domains on number of words correct per trial. There was a systematic improvement in speech perception with increases in entropy variance in both time and frequency domains. Entropy in real-world noise affects speech perception even when the overall signal-to-noise ratio is fixed. Experiments using real-world noise should consider this effect in their design and interpretation.