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952 results for “Noise”
Data from: Separate and combined effects of boat noise and a live crab predator on mussel valve gape behaviour
<p><strong>Data abstract:</strong></p> <p>Data on the valve gape of mussels and the behaviour of stimulus crabs during sound exposure trials. We provide the raw valve gape data, processed data, and scripts to process the raw data, make plots, and run the statistics.</p> <p><strong><span>Paper abstract:</span></strong></p> <p><span>Noisy human activities at sea are changing the acoustic environment, which has been shown to affect marine mammals and fishes. Invertebrates, such as bivalves, have so far received limited attention despite their important role in the marine ecosystem. Several studies have examined the impact of sound on anti-predator behaviour using simulated predators, but studies using live predators are scarce. In the current study, we examined the separate and combined effects of boat sound playback and predator cues from shore crabs (<em>Carcinus maenas</em>) on the behaviour of mussels (<em>Mytilus</em> spp.). We examined the behaviour of the mussels using a valve gape monitor and scored the behaviour from the crabs in one of two types of predator test conditions from video footage to control for effects from potential, sound-induced variation in crab behaviour. We found that mussels closed their valve gape during boat noise and with a crab in their tank, but also that the stimulus combination did not add up to an even smaller valve gape. The sound treatment did not affect the stimulus crabs, but the behaviour of the crabs did affect the valve gape of the mussels. Future research is needed to examine whether these results stand <em>in situ</em> and whether valve closure due to sound have fitness consequences for mussels. The effects on the well-being of individual mussels from anthropogenic noise may be relevant for population dynamics in the context of pressure from other stressors, their role as ecosystem engineers, and in the context of aquaculture.</span></p>
Vessel noise exposures of harbour seals from the Wadden Sea
<p>The North Sea faces intense ship traffic owing to increasing human activities at sea. As harbour seals (<em>Phoca vitulina</em>) are abundant top predators in the North Sea, it is hypothesised that they experience repeated, high-amplitude vessel exposures. Here, we test this hypothesis by quantifying vessel noise exposures from deployments of long-term sound and movement tags (DTAGs) on nine harbour seals from the Wadden Sea. An automated tool was developed to detect intervals of elevated noise in the sound recordings. An assessment by multiple raters was performed to classify the source as either vessels or other sounds. A total of 133 vessel passes were identified with received levels >97 dB re 1µPa RMS in the 2 kHz decidecade band and with ambient noise >6 dB below this detection threshold. Tagged seals spent most of their time within Marine Protected Areas (89 ± 13%, mean ± SD) and were exposed to high-amplitude vessel passes 4.3 ± 1.6 times per day. Only 32% of vessel passes were plausibly associated with an AIS-registered vessel. We conclude that seals in industrialized waters are exposed repeatedly to vessel noise, even in areas designated as protected, and that exposures are poorly predicted by AIS data.</p>
Designing single-cell experiments to harvest fluctuation information while rejecting measurement noise
<p>Numerical simulation outputs and model schematic figures for the Python portion (main text 3.1 and Supporting Information) in the preprint <em>Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise</em> Huy D. Vo, Linda Forero, Luis Aguilera, Brian Munsky bioRxiv doi: https://doi.org/10.1101/2021.05.11.443611</p>
Two-minute DAS ambient noise data recorded by Stanford DAS Array-1
<p>This repository contains the preprocessed 2-minute DAS ambient noise data recorded by the Stanford DAS Array-1 and the 1D shear-wave velocity model published in Spica et al. (2020) used in the manuscript entitled "On beamforming of DAS ambient noise recorded in an urban environment and Rayleigh-to-Love wave ratio estimation" by Yumin Zhao, Yunyue Elita Li, and Bei Li.</p>
Data for: Small reductions in cargo vessel speed dramatically reduce noise impacts to marine mammals
<p><span>Global reductions in the underwater radiated noise levels from cargo vessels are needed to reduce increasing cumulative impacts to marine wildlife. We use a vessel exposure simulation model to examine how reducing vessel source levels through slowdowns and technological modifications can lessen impacts on marine mammals. We show that the area exposed to ship noise reduces dramatically with moderate source level reductions that can be readily achieved with small reductions in speed. Moreover, slowdowns reduce all impacts to marine mammals despite the longer time that a slower vessel takes to pass an animal. We conclude that cumulative noise impacts from the global fleet can be reduced immediately by slowdowns. This solution requires no modification to ships and is scalable – from local speed reductions in sensitive areas to ocean basins. Speed reductions can be supplemented by routing vessels away from critical habitats and by technological modifications to reduce vessel noise.</span></p>
Mbit/s-range alkali vapour spin noise quantum random number generators - DATASETS
<p>Two example datasets. Each dataset consists of 2 files; one, where spin noise is present in the spectrum (noise_on.npy) and one, where spin noise is absent (noise_off.npy). The former is used generate random numbers, while the latter is used to 1. find a baseline threshold \Sigma, as described in the paper, and 2. to find the spin noise spectrum. </p> <p>The spin noise spectrum can be found by subtracting the spectra of the first and second file, as it is only present in the first file. Every dataset should be filtered around the Larmor frequency prior to any bit generation. The width of the band-pass filter should be such that the entirety of the spin noise spectrum is contained.</p> <p>Bit generation is to be done by protocols described in the paper "Mbit/s-range alkali vapour spin noise quantum random number generators". All bitrates are calculated at \Sigma = 5 \sigma, where \sigma is the standard deviation of noise_off.npy for the given experiment.</p> <p>Cs12: Sample rate 50 MHz. Measured at a temperature of approximately 140 degrees C. The Larmor frequency is approximately 670 kHz. There is a large induced gradient present in the system, so the spin noise spectrum is not a Lorentzian shape, but is spread with a full-width half max of 500 kHz . Protocol 1 raw bitrates are approximately 40 kb/s, whereas protocol 2 raw bitrates are approximately 2.40 Mb/s.</p> <p>Cs14: Sample rate 100 MHz. Spin noise measurement (noise_on.npy) has 1G samples (16 bit signed). Background measurement (noise_off.npy) has 10M samples (16 bit signed). Measured at a temperature of approximately 140 degrees C. The Larmor frequency is approximately 440kHz ,T2 is approximately 2e-6 s. This is the dataset presented in the paper. Protocol 1 raw bitrates are approximately 40 kb/s, protocol 2 raw bitrates are approximately 1.97 Mb/s.</p>
Psychoacoustic indicators of pass-by road traffic noise
<p>This dataset contains files related to a measurement campaign of pass-by road traffic noise using the Statistical Pass-By (SPB) method according to ISO 11819-1 (2023) on road surfaces in hot mix asphalt. </p> <p>The compressed folder "Audio files, timestamps and calibration file" is divided into subfolders per measurement day, named according to the measurement date and location. Each subfolder contains continuous recordings as .wav audio files and Excel files with timestamps at 2 seconds before (t0) and 2 s after (tf) the moment a vehicle passage produced the peak noise level in that audio file, besides the air temperature (Tair) and vehicle category (Category). The pass-bys are classified into four categories (Passenger cars, Vans, Dual-axle heavy vehicle - HD and Multiple-axle heavy vehicle - HM).</p> <p>The file "Calibration file 1Hz 113.7 dB.wav" is a pure-tone 113.7 dB calibration file recorded from the sound level meter and microphone used in the measurements.</p> <p>The Excel file "Psychoacoustic indicators of pass-by road traffic noise.xlsx" presents nine acoustic and psychoacoustic indicators calculated from the 4s audio excerpts of 2199 pass-by vehicle noise obtained from this measurement campaign. Calculations were conducted using the algorithms implemented on PsySound3. The columns in this Excel file mean the following:</p> <ol> <li>L<sub>A,max</sub>: average maximum A-weighted sound level, in dB</li> <li>ΔL: the difference between the peak L<sub>A,max </sub>and the L<sub>A,max</sub> at 2 s before the peak was reached, in dB</li> <li>L<sub>A,eq</sub>: A-weighted equivalent continuous sound level, in dB</li> <li>L10: 10% percentile statistical noise level, in dB</li> <li>L90: 90% percentile statistical noise level, in dB</li> <li>N50: 50% percentile loudness, in sone</li> <li>S50: 50% percentile sharpness, in acum</li> <li>R50: 50% percentile roughness, in asper</li> <li>FS50: 50% percentile fluctuation strength, in vacil</li> </ol>
Data and code from: Dynamics of cortical contrast adaptation predict perception of signals in noise
<p>Neurons throughout the sensory pathway adapt their responses depending on the statistical structure of the sensory environment. Contrast gain control is a form of adaptation in the auditory cortex, but it is unclear whether the dynamics of gain control reflect efficient adaptation, and whether they shape behavioral perception. Here, we trained mice to detect a target presented in background noise shortly after a change in the contrast of the background. The observed changes in cortical gain and behavioral detection followed the dynamics of a normative model of efficient contrast gain control; specifically, target detection and sensitivity improved slowly in low contrast, but degraded rapidly in high contrast. Auditory cortex was required for this task, and cortical responses were not only similarly affected by contrast but predicted variability in behavioral performance. Combined, our results demonstrate that dynamic gain adaptation supports efficient coding in auditory cortex and predicts the perception of sounds in noise.</p>
Migration of mechanical perturbations estimated by seismic coda wave interferometry during the 2018 pre-eruptive period at Kīlauea volcano, Hawaii : Noise Cross-correlation Functions, Seismic catalog, and GNSS data
<p>ARCHIVE_NCFs_KILAUEA_2018.zip : Compress folder with (1) the daily noise cross-correlation functions (in MSEED format) of the station pairs used in the paper and (2) the one hour noise cross-correlation functions (in H5 format) of the station pairs used in the figure 9 of the paper.</p> <p>Code_Data_HVO.ipynb : Code to download the seismic data, available on IRIS, used in this paper.</p> <p>GPS_data_AHUP.zip : Compress folder with the daily GPS data of the station AHUP used in the paper [Year, Month, Day, Day_of_the_year, Second_of_the_day, East_comp(mm), North_comp(mm), Vertical_comp(mm), Sig_East_comp, Sig_North_comp, Sig_Vertical_comp].</p> <p>Radial_tilt_UWD.txt : Daily radial tilt measurement of the tiltmeter UWD [Year, Month, Day, Radial_tilt(µrad)].</p> <p>Seismic_stations_Kilauea.txt : Name code and location of the seismic stations used in the paper [Station_code, Longitude, Latitude].</p> <p>Seismicity_Catalog_Kilauea_2018_USGS.txt : Seismic catalog from USGS used in the paper [Date_Time, Latitude, Longitude, Depth, Magnitude].</p>
Data repository of the paper "Quantum-noise-limited optical neural networks operating at a few quanta per activation"
<p>This data repository includes the requisite data and code for deriving the primary results from the paper, "Quantum-noise-limited optical neural networks operating at a few quanta per activation". The repository is structured to provide everything needed to reproduce the figures included in the main manuscript, along with the source code for training the neural network models and the collected experimental data mentioned in the paper.</p> <p>The code in this repository is primarily intended for reproducing the results discussed in the paper. Those interested in developing their own applications may refer to our Github repository: https://github.com/mcmahon-lab/Single-Photon-Detection-Neural-Networks.</p> <p><strong>Where to Start</strong></p> <p>The directory 'main_figures' includes Jupyter notebooks to generate each panel in Figure 3 and Figure 4 in the main text, using the data from the directory 'results', which can be generated by notebooks in the directory 'test'. </p> <p>The simulations, experiments, and figure generation were all conducted in Python. As certain parts of the code require specific versions of Python packages, the necessary packages are listed in the 'requirements.txt' file.</p> <p>For more information, please refer to 'README.txt'.</p>
Impulse sounds for the evaluation of impulse or transient noise reduction
<p>This data set includes nine impulse or transient sounds which have been used for the evaluation of impulse noise reduction in hearing aids.</p><p> </p><p><strong>Evaluation of impulse noise reduction in hearing aids with technical measurements and ratings of discomfort</strong></p><p>Hendrik Husstedt, Wiebke Hilgerdenaar, Marlitt Frenz, Florian Denk and Jürgen Tchorz</p><p>Acta Acust., 7 (2023) 47, DOI: <a href="https://doi.org/10.1051/aacus/2023042">https://doi.org/10.1051/aacus/2023042</a></p><p> </p><p> </p><p>All signals are provided as Wave files and "-mat" files. Each recording has a length of 155 ms and the impulse starts after 5 ms.</p><p>Wave files:</p><p>- full scale value corresponds to 200 Pa sound pressure or 140 dB SPL peak sound pressure level </p><p>- 96.000 sample rate</p><p>- 24 bits per sample</p><p>.mat file "sData.mat":</p><p>data.fs ... sample rate</p><p>data.t ... time vector to plot the signals</p><p>data.impulses(1).name ... name of the first impulse signal</p><p>data.impulses(1).signal ... first impulse signal</p><p>data.impulses(1).dBCpeak ... C-weighted peak sound pressure level of the first impulse signal</p><p> </p>
Cipher Machine TST 0605 Random Noise Generator 970 (Inv. 2017-426)
<p>This dataset represents the computed tomography image acquisition of a historical cipher machine from the collection of the Deutsches Museum. It is composed of CT reconstructed image stacks in the DICOM (.dcm) format.<br> It can be imported in any free or propietary CT-Viewer that supports the DICOM standard to generated 2D and 3D imaging.<br> If segmentations/ROIs are availabe, they are uploaded in a seperate image stacks and are a subset of the scanned cipher machine.</p> <p>object details:<br> name: TST 0605 Random Noise Generator 970<br> Inv.-No. of the Deutsches Museum: 2017-426</p> <p>file object details:<br> file format: image/dcm<br> pixel spacing unit: mm<br> X pixel spacing: 0.181<br> Y pixel spacing: 0.181<br> Z pixel spacing: 0.181<br> grid size x: 1788<br> grid size y: 1176<br> grid size z: 2524<br> color depth: 16Bit</p> <p>ownership & image acquisition:<br> project: <a href="https://digital.deutsches-museum.de/en/projects/3d-cipher">3D-Cipher</a><br> collection: <a href="https://digital.deutsches-museum.de">Deutsches Museum</a><br> IP holder: Deutsches Museum<br> license: <a href="https://creativecommons.org/licenses/by/4.0/deed.en">Creative Commons BY-SA 4.0</a><br> scanning facility: <a href="https://www.iis.fraunhofer.de/en/ff/zfp.html">Fraunhofer Development Center X-ray Technology EZRT/Fraunhofer IIS</a><br> scanning device: High Energy CT XXL-CT<br> Funding attribution: <a href="https://www.bmbf.de/bmbf/en">German Federal Ministry of Education and Research</a><br> acknowledgement: <a href="https://www.cryptomuseum.com">CryptoMuseum</a> (as main source for informations about the cipher machines)</p>
The second data release from the European Pulsar Timing Array II. Customised pulsar noise models for spatially correlated gravitational waves
<p>Aims: The nanohertz gravitational wave background (GWB) is expected to be an aggregate signal of an ensemble of gravitational waves emitted predominantly by a large population of coalescing supermassive black hole binaries in the centres of merging galaxies. Pulsar tiNanohertz ming arrays (PTAs), which are ensembles of extremely stable pulsars at approximately kiloparsec distances precisely monitored for decades, are the most precise experiments capable of detecting this background. However, the subtle imprints that the GWB induces on pulsar timing data are obscured by many sources of noise that occur on various timescales. These must be carefully modelled and mitigated to increase the sensitivity to the background signal. Methods: In this paper, we present a novel technique to estimate the optimal number of frequency coefficients for modelling achromatic and chromatic noise, while selecting the preferred set of noise models to use for each pulsar. We also incorporated a new model to fit for scattering variations in the Bayesian pulsar timing package temponest. These customised noise models enable a more robust characterisation of single-pulsar noise. We developed a software package based on tempo2 to create realistic simulations of European Pulsar Timing Array (EPTA) datasets that allowed us to test the efficacy of our noise modelling algorithms. Results: Using these techniques, we present an in-depth analysis of the noise properties of 25 millisecond pulsars (MSPs) that form the second data release (DR2) of the EPTA and investigate the effect of incorporating low-frequency data from the Indian Pulsar Timing Array collaboration for a common sample of ten MSPs. We used two packages, enterprise and temponest, to estimate our noise models and compare them with those reported using EPTA DR1. We find that, while in some pulsars we can successfully disentangle chromatic from achromatic noise owing to the wider frequency coverage in DR2, in others the noise models evolve in a much more complicated way. We also find evidence of long-term scattering variations in PSR J1600-3053. Through our simulations, we identify intrinsic biases in our current noise analysis techniques and discuss their effect on GWB searches. The analysis and results discussed in this article directly help to improve the sensitivity to the GWB signal and they are already being used as part of global PTA efforts.</p>
Data from: Electroencephalography Responses to Simplified Visual Signals Reveal Explain Differences in Speech-in-Noise Comprehension
<p>Contents and Folder Structure:</p> <p><strong>EEG Experiment</strong></p> <ul> <li><strong>EEG_stimuli</strong>: these are the videos that were presented to participants in the EEG experiment, and the code that generates them from the original corpus (link)</li> <li><strong>data_2020>split_trials</strong>: contains the raw EEG data starting 1.995s before each trial and ending 1.995s after each trial with naming convention subXx_VV_YY_N.fif where X or Xx is the subject number, YY is the modality condition (AV for audiovisual and V0 for video only), N is the trial number (between 0 and 4 inclusive), and VV is the video condition (1e for the envelope dot, 1m is the mismatched dot, 4v is the cartoon, bw is the edge detection and nh is the natural condition). <ul> <li><strong>unprocessed>raw</strong>: contains the unprocessed raw EEG data <ul> <li><strong>processed>Fs-200>BP-1-80-ASR-INTP-AVR</strong>: contains the pre-processed raw EEG data: the output of <em>run_preprocessing.m</em></li> <li><strong>processed>Fs-200>BP-1-80-ASR-INTP-AVR-ICr</strong>: contains the pre-processed raw EEG data after ICA cleaning: the output of <em>run_reject_ICs.m</em></li> <li><strong>stim>stim_dwnspl</strong>: contains the aligned 200Hz envelopes of the presented speech used as features for the time-lagged models</li> </ul> </li> </ul> </li> <li><strong>EEG_analysis_code </strong>[note: please extract the contents of this folder to match paths] <ul> <li><strong>2_ICA_filt:</strong> this folder contains the MATLAB code that performs the pre-processing of the EEG data, including filtering, downsampling, ICA cleaning etc. The main functions are: <ul> <li><em>run_preprocessing.m: downsampling, filtering, ASR cleaning</em></li> <li><em>run_reject_ICs.m: ICLabel ICA cleaning</em></li> </ul> </li> <li><strong>3_analysis:</strong> this is the Python code that performs the TRF and backward modelling on the EEG data. The main functions are: <ul> <li><em>multisensory_bw.py</em>: backwards model</li> <li><em>multisensory_fw.py</em>: forwards model</li> </ul> </li> </ul> </li> </ul> <p><strong>Behavioural Experiment</strong></p> <ul> <li><strong>behavioural</strong> <ul> <li><strong>0_dataset</strong>: these are the videos that were presented to participants in the behavioural experiment, and the code that generates them from the original corpus (AV GRID corpus)</li> <li><strong>3_analysis</strong>: behavioural data analysis script <ul> <li>main function: <em>data_grid_v3.py</em></li> </ul> </li> </ul> </li> <li><strong>behavioural_data>data_grid</strong>: behavioural results </li> </ul>
Data from: 3D shear-wave velocity model of central Makran using ambient-noise adjoint tomography
<p>The Makran subduction zone is unique in its wide onshore thick accretionary prism, and a volcanic arc not parallel to the E-W trend of the Makran accretionary prism. To investigate the internal structure of the accretionary prism, the crustal nature of Jaz Murian Depression, and the trend of the buried trench we have calculated a 3D shear-wave velocity model for a region around the border between eastern and western Makran using ambient-noise adjoint tomography and data from IASBS/CAM Makran temporary seismic network. In close agreement with previous works, our velocity model shows that the onshore accretionary prism consists of a low-velocity zone in the south and a high-velocity zone in the north with an average thickness of accreted sediments of 22 and 30 km, respectively. The young age of the surface rocks of the high-velocity part of the prism suggests the presence of a significant volume of igneous rocks scraped from the subducting oceanic slab. The velocity model indicates a continental crust of ~40 km with a thick sedimentary cover of ~20 km for the eastern part of Jaz Murian Depression. The presence of a NE-SW trending low-velocity region at a depth interval of 40-60 km subparallel with the trend of the volcanic arc, intermediate-depth earthquakes, and geometry of the overriding plate might be related to the trend of the buried trench. This implies that the observed NE-SW trending volcanic arc might be related to the geometry of the buried trench and not the eastward reduction of the subduction angle.</p>
Experimental and simulation data on magnetization and magnetization noise in tetris ice
<p>The files contain data presented in the "Deconstructing Magnetization Noise: Degeneracies, Phases, and Mobile Fractionalized Excitations in Tetris Artificial Spin Ice" paper.</p>
Data presented in the "Deconstructing Magnetization Noise: Degeneracies, Phases, and Mobile Fractionalized Excitations in Tetris Artificial Spin Ice" paper
<p>The files contain data presented in the "Deconstructing Magnetization Noise: Degeneracies, Phases, and Mobile Fractionalized Excitations in Tetris Artificial Spin Ice" paper.</p>
Antioxidation Medication for Noise-induced Hearing Loss
ClinicalTrials.gov study NCT00552786. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Micronutrients to Prevent Noise-induced Hearing Loss
ClinicalTrials.gov study NCT00808470. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Initial application of the noise-sorted scanning clustering algorithm to the analysis of composition-dependent organic aerosol thermal desorption measurements
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