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952 results for “Noise”
Inequalities in noise will affect urban wildlife
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Data from: Anthropogenic noise exposure over development increases baseline auditory activity and decision-making time in adult crickets
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Data from: Vessel noise prior to pile driving at offshore windfarm sites deters harbour porpoises from potential injury zones
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Anthropogenic noise, song, and territorial aggression in southern house wrens
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Characterising underwater noise and changes in harbour porpoise behaviour during the decommissioning of an oil and gas platform
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Data and code for figures: Ultralow-Noise Photonic Microwave Synthesis using a Soliton Microcomb-based Transfer Oscillator
<p>This repository contains the data and code for the paper "Ultralow-Noise Photonic Microwave Synthesis using a Soliton Microcomb-based Transfer Oscillator".</p>
Bio-cementation and crackling noise from calcareous sand and consolidated aggregates
<p>The supporting data for "Bio-cementation and crackling noise from calcareous sand and consolidated aggregates".</p>
Data from: Social context and noise affect within and between-male song adjustments in a common passerine
Across populations, animals that inhabit areas with high anthropogenic noise produce vocalizations that differ from those inhabiting less noisy environments. Such patterns may be due to individuals rapidly adjusting their songs in response to changing noise, but individual variation is seldom explored. We tested the hypothesis that male house wrens (Troglodytes aedon) immediately adjust their songs according to changing noise, and that social context further modifies responses. We recorded songs, quantified noise, and defined social context within pairs as female fertile status and between males as number of conspecific neighbors. We used a reaction norm approach to compare song trait intercepts (between-male effects) and slopes (within-male effects) as a function of noise. Individuals immediately adjusted song duration in response to changing noise. How they achieved adjustments varied: some sang shorter and others longer songs with greater noise, and individuals varied in the extent to which they adjusted song duration. Variation in song duration could be affected by competition, as between-male noise levels interacted with number of neighbors to affect syllable duration. Neither within- nor between-male noise effects were detected for frequency traits. Rather males with fertile mates sang lower frequency songs and increased peak frequency with more neighbors. Among males, social context but not noise affected song frequency, whereas temporal structure varied between and within individuals depending on noise and social factors. Not all males adjusted signals the same way in response to noise and selection could favor different levels of variation according to noise.
Efficient learning of quantum noise
<p>Noise is the central obstacle to building large-scale quantum computers. Quantum systems with sufficiently uncorrelated and weak noise could be used to solve computational problems that are intractable with current digital computers. There has been substantial progress towards engineering such systems. However, continued progress depends on the ability to characterize quantum noise reliably and efficiently with high precision. Here we introduce a protocol that comprehensively and efficiently characterizes the error rates of quantum noise and we experimentally implement it on a 14-qubit superconducting quantum architecture. The method returns an estimate of the effective noise with relative precision and can detect arbitrary correlated errors. We show how to construct a quantum noise correlation matrix allowing the easy visualization of all pairwise correlated errors, enabling the discovery of long-range two-qubit correlations in the 14 qubit device that had not previously been detected. These properties of the protocol make it exceptionally well suited for high-precision noise metrology in quantum information processors. Our results are the first implementation of a provably rigorous, full diagnostic protocol capable of being run on state of the art devices and beyond. These results pave the way for noise metrology in next-generation quantum devices, calibration in the presence of crosstalk, bespoke quantum error-correcting codes, and customized fault-tolerance protocols that can greatly reduce the overhead in a quantum computation.</p>
The role of isochrony in speech perception in noise - Dataset
<p>This dataset contains speech stimuli and listener data reported on in Aubanel & Schwartz (2020), DOI: <a href="http://dx.doi.org/10.1038/s41598-020-76594-1">10.1038/s41598-020-76594-1</a>. </p> <p><strong>French data</strong></p> <ul> <li>French sentences are taken from the Fharvard corpus (Aubanel et al., 2020, DOI: <a href="https://dx.doi.org/10.1016/j.specom.2020.07.004">10.1016/j.specom.2020.07.004</a>)</li> <li>Speech material and sentence recordings are available at: <a href="https://dx.doi.org/10.5281/zenodo.1462854">10.5281/zenodo.1462854</a></li> <li><strong>fr_stimuli.zip</strong> contains the stimuli presented to the listeners</li> <li><strong>fr_responses.csv</strong> contains the responses typed by listeners</li> </ul> <p><strong>English data</strong></p> <ul> <li>English sentences are taken from the Harvard corpus (Rothauser et al. 1969)</li> <li>Speech material and sentence recordings are taken from the MAVA corpus, available at: <a href="https://dx.doi.org/10.4227/139/59a4c21a896a3">10.4227/139/59a4c21a896a3</a></li> <li><strong>en_stimuli.zip</strong> contains the stimuli presented to the listeners</li> <li><strong>en_responses.csv</strong> contains the responses typed by listeners</li> </ul> <p> </p>
Data from: Combined effect of anthropogenic noise and artificial night lighting negatively affect Western Bluebird chick development
<p>Sensory pollutants such as anthropogenic noise and night lighting now expose much of the world to evolutionarily novel sound and night lighting conditions. An emerging body of literature has reported a variety of deleterious effects caused by these stimuli, spanning behavioral, physiological, population and community-level responses. However, the combined influence of noise and light has received almost no attention despite the co-occurrence of these stimuli in many landscapes. Here we evaluated the singular and combined effects of these stimuli on Western Bluebird (<i>Sialia mexicana</i>) reproductive success using a field-based manipulation. Nests exposed to noise and light together experienced less predation than control and light-exposed nests, and noise-exposed nests experienced less predation than control nests, yet overall nest success was only higher in noise-exposed nests compared to light-exposed nests. Although exposure to light decreased nestling body condition and evidence was mixed for the singular effects of noise or light on nestling size, those nestlings exposed to noise and light together were smaller across several metrics than nestlings in control nests. Our results support previous research on the singular effects of either stimuli, including potential benefits, such as reduced nest predation with noise exposure. However, our results also suggest that noise and light together can negatively affect some aspects of reproduction more strongly than either sensory pollutant alone. This finding is especially important given that these stimuli tend to covary and are projected to increase dramatically in the next several decades.</p>
Associated dataset for "Instrumental Evaluation of Sensor Self-Noise in Binaural Rendering of Spherical Microphone Array Signals"
<p>The conducted instrumental evaluation utilizes the Real-Time Spherical Microphone Renderer (<a href="https://github.com/AppliedAcousticsChalmers/ReTiSAR">ReTiSAR</a>) for binaural reproduction in Python. The at that time employed code state should be used in order to exactly reproduce the rendering results in this data set. The frozen code state for this data set is available at:<br> <a href="https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2020.FA">https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2020.FA</a></p> <p>Download the rendering pipeline and follow the setup instructions! Use the here included Conda environment file when setting up the Python environment. In this way, you will obtain exactly the same Python setup as utilized in the instrumental evaluation in the publication:</p> <pre><code class="language-bash">conda env create --file ReTiSAR_environment_freeze.yml</code></pre> <pre><code class="language-bash">source activate ReTiSAR_FA_freeze</code></pre> <p>Directory "SMA sampling grids":</p> <ul> <li>Visualization of spatial arrangement (like Figure 4) for all investigated spherical microphone array rendering configurations (Table 1)</li> </ul> <p>Shell script "record_snr.sh":</p> <ul> <li>Record the input and output signals of the rendering pipeline for sound field (target / wanted) and self-noise (unwanted) components for all configurations at multiple head orientations</li> <li>All captured signals are contained in the "SNR" directory</li> </ul> <p>Matlab script "calculate_snr.m":</p> <ul> <li>Visualize the raw captured input and output signals (like Figure 1 for all configurations)</li> <li>Visualize the resulting signal-to-noise ratio (like Figure 2 for all configurations)</li> <li>Visualize the comparison of the resulting signal-to-noise ratio of all configurations (Figure 3, also for the resulting SNR from signals with A-weighting)</li> <li>All generated plots are contained in the "SNR" directory</li> </ul> <p>Shell script "record_noise.sh":</p> <ul> <li>Record the calibration and noise signals of the mh acoustic Eigenmike 32 spherical microphone array in the anechoic chamber at Chalmers University of Technology (Appendix)</li> <li>All captured signals are contained in the "EM32 measurements" directory</li> <li>Pictures of the measurement setup are contained in the "Pictures" subdirectory</li> </ul> <p>Matlab script "calculate_EM32_noise_levels.m":</p> <ul> <li>Determine the resulting target signal sensitivity and equivalent input noise levels for the investigated pre-amplification gains (Table 2)</li> <li>Visualize the statistical distribution of the individual raw and weighted SMA channels (like Figure 6 for all configurations)</li> <li>Visualize the spatial distribution of the individual raw and weighted SMA channels for all configurations</li> <li>Visualize the smoothed and averaged magnitude spectra of the individual raw and weighted SMA channels (like Figure 5 for all configurations)</li> </ul>
Accuracy of the Group Velocity of Love Waves Extracted from Ambient Seismic Noise
<p>Love wave waveforms derived from the empirical Green's functions and Ground Truth earthquake in my manuscript submitted to Journal of Geophysical Research: Solid Earth.</p>
Data from: Sound settlement: noise surpasses land cover in explaining breeding habitat selection of secondary cavity-nesting birds
Birds breeding in heterogeneous landscapes select nest sites by cueing in on a variety of factors from landscape features and social information to the presence of natural enemies. We focus on determining the relative impact of anthropogenic noise on nest site occupancy, compared to amount of forest cover, which is known to strongly influence the selection process. We examine chronic, industrial noise from natural gas wells directly measured at the nest box as well as site-averaged noise, using a well-established field experimental system in northwestern New Mexico. We hypothesized that high levels of noise, both at the nest site and in the environment, would decrease nest box occupancy. We set up nest boxes using a geospatially paired control and experimental site design and analyzed four years of occupancy data from four secondary cavity-nesting birds common to the Colorado Plateau. We found different effects of noise and landscape features depending on species, with strong effects of noise observed in breeding habitat selection of Myiarchus cinerascens, the Ash-throated Flycatcher, and Sialia currucoides, the Mountain Bluebird. In contrast, the amount of forest cover less frequently explained habitat selection for those species or had a smaller standardized effect than the acoustic environment. Although forest cover characterization and management is commonly employed by natural resource managers, our results show that characterizing and managing the acoustic environment should be an important tool in protected area management.
Synthetic realistic noise-corrupted PPG database and noise generator for the evaluation of PPG denoising and delineation algorithms
<p><strong>Overview </strong></p> <p>This database is meant to evaluate the performance of denoising and delineation algorithms for PPG signals affected by noise. The noise generator allows applying the algorithms under test to an artificially corrupted reference PPG signal and comparing its output to the output obtained with the original signal. Moreover, the noise generator can produce artifacts of variable intensities, permitting the evaluation of the algorithms' performance against different noise levels. The reference signal is a PPG sample of a healthy subject at rest during a relaxing session.</p> <p> </p> <p><strong>Database</strong></p> <p>The database includes 1 recording of 72 seconds of synchronous PPG and ECG signals sampled at 250 Hz using a Medicom device, ABP-10 module (Medicom MTD Ltd., Russia). It was collected from a healthy subject during an induced relaxation by guided autogenic relaxation. For more information about the data collection, please refer to the following publication: <a href="https://pubmed.ncbi.nlm.nih.gov/30094756/">https://pubmed.ncbi.nlm.nih.gov/30094756/</a></p> <p>In addition, PPG signals corrupted by the noise generator at different levels are also included in the database.</p> <p> </p> <p><strong>Realistic noise generator</strong></p> <p>Motion Artifacts in PPG signals generally appear in the form of sudden spikes (in correspondence to the subject's movement) and slowly varying offsets (baseline wander) due to the changes in distance between the skin and the sensor after every sudden movement. For this reason, conventional noise generators — using random noise drawn from different distributions such as Gaussian or Poissonian — do not allow to properly evaluate the algorithm's performance, as they can only provide unrealistic noises compared to the one commonly found in PPG signals. To overcome this issue, we designed a more realistic synthetic noise generator that can simulate those two behaviors, enabling us to corrupt a reference signal with different noise levels. The details about noise generation are available in the reference paper.</p> <p> </p> <p><strong>Data Files</strong></p> <p>The reference PPG signal can be found in <em>Datasets\GoodSignals\PPG</em> and the simultaneously acquired ECG in <em>Datasets\GoodSignals\ECG</em>. The folder <em>Datasets\NoisySignals</em> contains 340 noisy PPG signals affected by different levels of noise. The names describe the intensity of the noise (evaluated in terms of the standard deviation of the random noise used as input for the noise generator, see reference paper). Five noisy signals are produced for every noise level by running the noise generator with five random seeds each (for noise generation).</p> <p>Name convention: <em>ppg_stdx_y</em> denotes the y-th noisy PPG signal produced using a noise with a standard deviation of x.</p> <p><em>Datasets\BPMs</em> contains the ground truth for the heart-rate estimation computed in windows of 8s with an overlap of 2s.</p> <p><strong>Code</strong></p> <p>The folder <em>Code </em>contains the MATLAB scripts to generate the noisy files by generating the realistic noise with the function noiseGenerator.</p> <p><strong>When referencing this material, please cite:</strong></p> <p>Masinelli, G.; Dell'Agnola, F.; Valdés, A.A.; Atienza, D. SPARE: A Spectral Peak Recovery Algorithm for PPG Signals Pulsewave Reconstruction in Multimodal Wearable Devices. <em>Sensors</em> <strong>2021</strong>, <em>21</em>, 2725. <a href="https://doi.org/10.3390/s21082725">https://doi.org/10.3390/s21082725</a></p>
Data from: Vocal traits and diet explain avian sensitivities to anthropogenic noise
Global population growth has caused extensive human-induced environmental change, including a near-ubiquitous transformation of the acoustical environment due to the propagation of anthropogenic noise. Because the acoustical environment is a critical ecological dimension for countless species to obtain, interpret and respond to environmental cues, highly novel environmental acoustics have the potential to negatively impact organisms that use acoustics for a variety of functions, such as communication and predator/prey detection. Using a comparative approach with 308 populations of 183 bird species from 14 locations in Europe, North American and the Caribbean, I sought to reveal the intrinsic and extrinsic factors responsible for avian sensitivities to anthropogenic noise as measured by their habitat use in noisy versus adjacent quiet locations. Birds across all locations tended to avoid noisy areas, but trait-specific differences emerged. Vocal frequency, diet and foraging location predicted patterns of habitat use in response to anthropogenic noise, but body size, nest placement and type, other vocal features and the type of anthropogenic noise (chronic industrial vs. intermittent urban/traffic noise) failed to explain variation in habitat use. Strongly supported models also indicated the relationship between sensitivity to noise and predictive traits had little to no phylogenetic structure. In general, traits associated with hearing were strong predictors – species with low-frequency vocalizations, which experience greater spectral overlap with low-frequency anthropogenic noise tend to avoid noisy areas, whereas species with higher frequency vocalizations respond less severely. Additionally, omnivorous species and those with animal-based diets were more sensitive to noise than birds with plant-based diets, likely because noise may interfere with the use of audition in multimodal prey detection. Collectively, these results suggest that anthropogenic noise is a powerful sensory pollutant that can filter avian communities nonrandomly by interfering with birds' abilities to receive, respond to and dispatch acoustic cues and signals.
Data from: Chronic anthropogenic noise disrupts glucocorticoid signaling and has multiple effects on fitness in an avian community
Anthropogenic noise is a pervasive pollutant that decreases environmental quality by disrupting a suite of behaviors vital to perception and communication. However, even within populations of noise-sensitive species, individuals still select breeding sites located within areas exposed to high noise levels, with largely unknown physiological and fitness consequences. We use a study system in the natural gas fields of northern New Mexico to test the prediction that exposure to noise causes glucocorticoid-signaling dysfunction and decreases fitness in a community of secondary cavity nesting birds. In accordance with these predictions, and across all species, we find strong support for noise exposure decreasing baseline corticosterone in adults and nestlings and, conversely, increasing acute stressor-induced corticosterone in nestlings. We also document fitness consequences with increased noise in the form of reduced hatching success in the western bluebird (Sialia mexicana), the species most likely to nest in noisiest environments. Nestlings of all three species exhibited accelerated growth of both feathers and body size at intermediate noise amplitudes compared to low or higher amplitudes. Our results are consistent with recent experimental laboratory studies, and show that noise functions as a chronic, inescapable stressor. Anthropogenic noise likely impairs environmental risk perception by species relying on acoustic cues, and, ultimately, leads to impacts on fitness. Our work, when taken together with recent efforts to document noise across the landscape, implies potential widespread, noise-induced chronic stress coupled with reduced fitness for many species reliant on acoustic cues.
Data from: Finding stories in noise: mitochondrial portraits from RAD data
Mitochondrial DNA (mtDNA) has formed the backbone of phylogeographic research for many years, however, recent trends focus on genome-wide analyses. One method proposed for calibrating inferences from noisy Next-Generation data, such as RAD sequencing, is to compare these results with analyses of mitochondrial sequences. Most researchers using this approach appear to be unaware that many Single Nucleotide Polymorphisms (SNPs) identified from genome-wide sequence data are themselves mitochondrial, or assume that these are too few to bias analyses. Here we demonstrate two methods for mining mitochondrial markers using RAD sequence data from three South African species of yellowfish, Labeobarbus. First, we use a rigorous SNP discovery pipeline using the program STACKS, to identify variant sites in mtDNA, which we then combine into haplotypes. Secondly, we directly map sequence reads against a mitochondrial genome reference. This method allowed us to reconstruct up to 98% of the Labeobarbus mitogenome. We validated these mitogenome reconstructions through BLAST database searches and by comparisons with cytochrome b gene sequences obtained through Sanger sequencing. Finally, we investigate the organismal consequences of these data including ancient genetic exchange and a recent translocation among populations of L. natalensis, as well as interspecific hybridisation between L. aeneus and L. kimberleyensis.
Data from: Acoustically advertising male harbour seals in southeast Alaska do not make biologically relevant acoustic adjustments in the presence of vessel noise
Aquatically breeding harbour seal (Phoca vitulina) males use underwater vocalizations during the breeding season to establish underwater territories, defend territories against intruder males, and possibly to attract females. Vessel noise overlaps in frequency with these vocalizations and could negatively impact breeding success by limiting communication space. In this study we investigated whether harbour seals employed anti-masking strategies to maintain communication in the presence of vessel noise in Glacier Bay National Park and Preserve, Alaska. Harbour seals in this location did not sufficiently adjust source levels or acoustic parameters of vocalizations to compensate for acoustic masking. Instead, for every 1 dB increase in ambient noise, signal excess decreased by 0.84 dB, indicating a reduction in communication space when vessels passed. We suggest that harbour seals may already be acoustically advertising at or near a biologically maximal sound level, and therefore lack the ability to increase call amplitude to adjust to changes in their acoustic environment. This may have significant implications for this aquatically breeding pinniped, particularly for populations in high noise regions.
Anthropogenic noise is associated with telomere length and carotenoid-based coloration in free-living nestling songbirds
<p>Growing evidence suggests that anthropogenic noise has deleterious effects on the behavior and physiology of free-living animals. These effects may be particularly pronounced early in life, when developmental trajectories are sensitive to stressors, yet studies investigating developmental effects of noise exposure in free-living populations remain scarce. To elucidate the effects of noise exposure during development, we examined whether noise exposure is associated with shorter telomeres, duller carotenoid-based coloration and reduced body mass in nestlings of a common urban bird, the great tit (Parus major). We also assessed how the noise environment is related to reproductive success. We obtained long-term measurements of the noise environment, over a ~24-h period, and characterized both the amplitude (measured by LAeq, LA90, LA10, LAmax) and variance in noise levels, since more stochastic, as well as louder, noise regimes might be more likely to induce stress. In our urban population, noise levels varied substantially, with louder, but less variable, noise characteristic of areas adjacent to a highway. Noise levels were also highly repeatable, suggesting that individuals experience consistent differences in noise exposure. The amplitude of noise near nest boxes was associated with shorter telomeres among smaller, but not larger, brood members. In addition, carotenoid chroma and hue were positively associated with variance in average and maximum noise levels, and average reflectance was negatively associated with variance in background noise. Independent of noise, hue was positively related to telomere length. Nestling mass and reproductive success were unaffected by noise exposure. Results indicate that multiple dimensions of the noise environment, or factors associated with the noise environment, could affect the phenotype of developing organisms, that noise exposure, or correlated variables, might have the strongest effects on sensitive groups of individuals, and that carotenoid hue could serve as a signal of early-life telomere length.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.