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952 results for “noise”
Associated dataset for "Evaluation of Sensor Self-Noise in Binaural Rendering of Spherical Microphone Array Signals"
<p>The conducted instrumental and perceptual evaluation utilize the Real-Time Spherical Microphone Renderer (<a href="https://github.com/AppliedAcousticsChalmers/ReTiSAR">ReTiSAR</a>) for binaural reproduction in Python. However, the provided execution configurations (see below) are probably not exactly in accordance with the latest ReTiSAR code base. Hence, the at the 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.ICASSP">https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2020.ICASSP</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 should obtain exactly the same Python setup as utilized in the instrumental and perceptual evaluation in the publication:</p> <pre><code>conda env create --file ReTiSAR_environment_freeze.yml</code></pre> <pre><code>source activate ReTiSAR_ICASSP_freeze</code></pre> <p>Directory "SNR":</p> <ul> <li>Tools for instrumental evaluation (Section 4)</li> <li>Shell script to capture input and output signals of rendering pipeline for sound field (target / wanted) and self-noise (unwanted) components for all specified configurations</li> <li>Matlab script to analyse captured signal and generate system transfer plots (Figure 1 to Figure 3 and further configurations)</li> </ul> <p>Directory "Relative Output Levels":</p> <ul> <li>Tools for preparation of perceptual evaluation (Section 5)</li> <li>Shell script to capture rendered uniformly contributing noise signals for all specified configurations</li> <li>Matlab script to analyse and level align captured signals and generate plot result plot (Figure 4)</li> </ul> <p>Directory "Absolute Output Levels":</p> <ul> <li>Tools for specification of perceptual evaluation (Section 5)</li> <li>Shell script to capture reproduced uniformly contributing noise signals for all specified configurations</li> <li>Matlab script to analyse the calibrated captured signals yielding the average level in the ear signals of 58.2 dBSPL (Section 5.1)</li> </ul> <p>Files in base directory and directory "Study Results":</p> <ul> <li>Tools for perceptual evaluation / user study (Section 5)</li> <li>Matlab GUI to conduct perceptual user study (employ by executing "ICASSP_gui.m", respective ReTiSAR instances are started and remote controlled by the GUI, raw study results will be stored in "results" directory)</li> <li>Matlab script to "calculate_conclusion.m" to analyse the raw study results and generate individual and conclusive result plots (Figure 5, Figure 6 and more)</li> </ul>
Short-Term Synchronous and Asynchronous Ambient Noise Tomography in Urban Areas: Application to Karst Investigation
<p>We used DSurfTomo (<a href="https://github.com/HongjianFang/DSurfTomo">HongjianFang/DSurfTomo: Direct inversion of surface dispersion data based on ray tracing (github.com)</a>) for the tomography.</p> <p>ABC2_2023.dat is the travel time of C1 and C2 cross-correlation functions, used in our tomography.</p> <p>ManualDSurfTomoV1.3.pdf is the manual of DSurfTomo, including the data format description for ABC2_2023.dat.</p> <p>yunqiVs3D.txt is the interpolated 3D Vs model, including longitude, latitude, depth (meter), Vs (m/s).</p> <p>Previous version error: I forgot to write the Vs value.</p>
X-ray diffraction dataset for experimental noise filtering
<p>X-ray diffraction data set for the training of noise filtering algorithms. The data set contains groups of low- and high-counting statistics pairs. The sampling times are mostly 1 (20) seconds for low (high) counting data. Three files in HDF5 format are provided, corresponding to a training, validation and test data set. Each data group contains sequences of 41 consecutive frames, corresponding to a scan along the reciprocal h-direction. Next to the raw data, sampling times and monitor values are included. The test data set additionally contains denoised low-count frames obtained from a pre-trained neural network.</p> <p>Additionally, files containing the trained model weights are included for two different architectures described in the main article (10.1038/s42256-024-00790-1).</p> <p>The data has been recorded on a La<sub>1.88</sub>Sr<sub>0.12</sub>CuO<sub>4</sub> single crystal at the beamline P21.1 at the PETRA III storage ring at DESY in Hamburg, Germany. The scattering intensities were recorded using Dectris Pilatus 100K CdTe detector. The diffractometer was operated with 100 keV photons and the sample was cooled to T ~ 30 K. The data contains different signals such as weak 2D charge density wave order, fundamental Bragg peaks, powder lines, spurions and dead pixels.</p>
Dataset for manuscript "During haptic communication, the central nervous system compensates distinctly for delay and noise"
<p>Data relating to the manuscript "Dataset for manuscript "During haptic communication, the central nervous system compensates distinctly for delay and noise". This includes the experiment dataset (in file experiment_dataset.csv) as well as the MATLAB functions used for the development of the simulation model (with main function main_delay.m)</p>
Underwater noise from ships during 2014-2020
<p>These data accompany the manuscript "Underwater noise emissions from ships during 2014-2020" submitted for publication in Environmental Pollution. These data consist of daily emissions of underwater noise emissions from global shipping, reported as noise energy in Joules, in three frequencies (63, 125 and 2000 Hz). The data is provided in zip compressed gridded binary netcdf3 data format using Climate & Forecast conventions. </p>
Dataset of handling noise for "Assessing the relevance of perceptually driven objective metrics in the presence of handling noise"
<p>This dataset contains the handling noise chunks used in [1] under the<br> folders "tapping" and "rustle" and the csv files used to compute the<br> results in section 5 of [1] under the "csv" folder. These chunks have<br> been extracted from seven in-house recordings and twelve of the sixteen<br> recordings from [2]. The twelve recordings from [2] have first been<br> converted from the .wma to the .wav format and resampled from 44100 Hz<br> to 48000 Hz before the extraction of the chunks. The sample rate of the<br> in-house recordings is natively 48000 Hz.</p> <p><br> [1] Angonin, C., Chourdakis E. T., and Åeng, R. A. "Assessing the<br> relevance of perceptually driven objective metrics in the presence of<br> handling noise", in 152nd Audio Engineering Society Convention,<br> Netherlands, 2022.</p> <p>[2] Kentric, P. Jackson, I. R., Fazenda, B. M, Cox, T. J., and Li, F. F.<br> "Microphone handling noise: Measurements of perceptual threshold and effects<br> on audio quality." PloS one, 10(10), 2015.</p> <p> </p>
Underwater noise from ships during 2014-2020
<p>These data accompany the manuscript "Underwater noise emissions from ships during 2014-2020" submitted for publication in Environmental Pollution. These data consist of daily emissions of underwater noise emissions from global shipping, reported as noise energy in Joules, in three frequencies (63, 125 and 2000 Hz). The data is provided in zip compressed gridded binary netcdf3 data format using Climate & Forecast conventions. <br> </p>
Data from microphone to measure the noise generated by the mobilefuge
<p>The two datasets uploaded are the measurement of noise generated when the mobilefuge is placed on table with a damping pad or without a damping pad. We found that with the use of the damping pad, the noise recorded in the microphone decreased by 13dB indicating the improved stable operation of the mobilefuge.</p>
Low Frequency Ambient Noise Dynamics and Trends: Cape Leeuwin, Australia
<p>Data files and code for : Low Frequency Ambient Noise Dynamics and Trends: Cape Leeuwin, Australia</p> <p>R version 4.X used for plots and analysis. Packages: entropy, foreach, rEDM</p> <ol> <li>Plots.R : Create plots for manuscript. </li> <li>MI.R : Evaluate lagged Mutual Information of random data segments</li> <li>CCM.R : Convergent cross mapping of Ambient Noise with teleconnections</li> <li>EMM_Surrogate.R : Evaluate CCM significance with randomized surrogates</li> </ol> <p>Python 3.X used for plots and analysis. Packages: pandas, matplotlib, emd, pyEDM</p> <ol> <li>Figure5,py Figure14.py : Create plots for manuscript</li> <li>EMD.py : Compute empirical mode decompositions</li> </ol> <p> </p>
labordynamicsinstitute/rampnoise: Code for Multiplicative Noise Infusion
<p>First proposed by Evans, Zayatz and Slanta (1998), multiplicative input noise infusion is used as a disclosure-avoidance measure. See also our implementation in the Quarterly Workforce Indicators (published in 2009, but first implemented in 2003). This repository illustrates noise infusion with some toy data.</p>
Comparing recent PTA results on the nanohertz stochastic gravitational wave background - full noise and GWB parameter comparison plots
<p>A full collection of plots comparing the noise properties of individual pulsars and gravitational wave background parameters discussed in the companion paper <em>Comparing recent PTA results on the nanohertz stochastic gravitational wave background</em> (IPTA 2024).</p> <p><code>Section4_GWB_comparison.zip</code> supplements and expands section 4.1, "Comparing the published GWB measurements," of IPTA (2024). It contains parameter difference distributions for GWB model parameters. There are four different models included. The HD correlated powerlaw (PL) model make up the basis for Figure 2. Additionally, there are three comparisons not included in IPTA (2024). First, comparisons the common uncorrelated red noise (CURN) PL model are included. Finally, comparisons of two free spectral (FS) models (HD and CURN) are included. These comparisons fit the HD and CURN FS posteriors using the <code>ceffyl</code> software package, and then compare the parameters of the resulting powerlaw fits.</p> <p><code>Section5_Noise_comparison.zip</code> supplements section 5, "Comparing Pulsar Noice Properties," of IPTA (2024). It contains plots for 27 pulsars timed by more than one PTA collaboration, including the plots for PSR J1012+5307, which are presented in Figure 7. The plots include noise parameter posteriors, time domain GP realizations, TOA residuals, and TOA radio frequency.</p>
Cockpit noise Falcon 2000LXS TRH-OSL CleanSky2 VOICI Data 1
<p>Data file: <em>Recording 6_low_freq_boost_remove_speech_2_WoodPk_reinsert_16b.wav<br> Extended description file: <em>Cockpit noise Falcon 2000LXS TRH-OSL description.pdf</em></em></p> <p><strong>Scenario</strong>:</p> <p>Sound recording during flight Trondheim – Oslo by Rely AS 2018-09-13, using a Falcon 2000LXS (2017). The full flight, gate to gate, is included.</p> <p><strong>Recording and calibration</strong>:</p> <p>A MicW i436 microphone with windshield was placed just in front of the airplane throttle. The microphone was connected to an Apple iPod, operated by pilots. Signal sample values on the .wav -file can be converted to sound pressure in Pa by multiplying by 13.79.</p> <p><strong>Post-processing</strong>:</p> <ol> <li>The following signal components have been removed from the recording, and replaced by nearby background noise:<br> - Pilot speech<br> - ATC communication<br> - Voice messages automatically generated by the aircraft (e.g., altitude)</li> <li>The pitch trim confirmation signal has first been removed and then re-inserted at the correct level, but in a version recorded 30 cm from the cockpit loudspeakers.</li> <li>The recording setup has a high-pass function with cut-off at 150 Hz. A gentle boost has been applied below this frequency.</li> </ol>
Reducing cardiac-induced noise in brain maps of R2* and magnetic susceptibility
<p>This high resolution dataset contain MR images of one participant acquired with a standard linear sampling and a cartesian pseudo-spiral sampling, with 3 repetitions each. It also contain the corresponding R2* and QSM maps shown in the paper. The data are presented both as .nii and .mat files.</p>
Quantum Data Centres in the Presence of Noise data
<p>This dataset contains all of the raw data needed to reproduce the work in </p> <table> <tbody> <tr> <td><span><a href="https://arxiv.org/abs/2407.10769">arXiv:2407.10769</a></span></td> </tr> </tbody> </table> <p>and any future publications of that work. It also includes data on a variety of edge cases and related investigations that could not be included in the main paper.</p> <p>The file names and directory names are verbose and intended to indicate what they contain.</p> <p>The terms F_werner and F_w refer to the Werner state fidelity and are used interchangeably. p_error and p_depolar_error_cnot, etc are also interchangeable and refer to the error probability of imperfect two-qubit gate operation (this appears as \epsilon_{cnot} in the aforementioned paper). </p> <p>alpha, where it appears, refers to the coefficient of |0> in the input state of the control qubit when a single remote CNOT gate is considered. Phase refers to the relative phase between the coefficients of |0> and |1> in the same context. These parameters also appears in some of the data from larger circuits (marked MQT bench after the benchmarking suite that the circuits come from) but should be ignored as they do not influence the result in that case and are instead indicative only of certain (now deprecated) simulator settings (indicating the possibility of creating the relevant input state) when the data was taken. The inclusion of those parameters in the data files was erroneous. The input state indicated by the parameters in that context was not inputted to the circuit. Instead the circuits were initialised as described in the relevant circuit description. The relevant QASM files can be found on the MQT bench website, which is formally referenced in the work cited above. </p>
Raw and post-processed data for the microscopic investigation of the effect of random envelope fluctuations on phoneme-in-noise perception
<p>The current dataset consists of three main folders:</p> <ul> <li><strong>01-Stimuli/</strong>: Contains the three sets of noises (white noise, bump noise, MPS noise) for the 12 study participants (S01 to S12).</li> <li><strong>02-Raw-data/fastACI/</strong>: Contains the raw data as obtained for each participant, which are also available within the GitHub repository of the fastACI toolbox, using the same directory tree. The results for each (anonymised) participant (under: <strong>publ_osses2022b/data_SXX/1-experimental_results/</strong>) include their audiometric thresholds (folder: <strong>audiometry</strong>), the results for the Intellitest speech test (folder: <strong>intellitest</strong>), and for the phoneme-in-noise test /aba/-/ada/ for the three noises (savegame files in MAT format).</li> <li><strong>02-Raw-data/ACI_sim/</strong>: Contains the raw data as obtained for the artificial listener, i.e., the model osses2022a.m (available within the fastACI toolbox). Twelve sets of simulations (using the waveforms of participants S01 to S12) were run for the three types of test noises. The results of the simulations of the phoneme-in-noise test are stored in the savegame MAT files. The template derived from 100 repetitions of /aba/ and /aba/ at an SNR=-6 dB in white noise is also included (template-osses2022a-speechACI_Logatome-abda-S43M-trial-1-v1-white-2022-7-15-N-0100.mat). The same template was used in all simulations.</li> <li><strong>03-Post-proc-data/ACI_exp/</strong>: Auditory classification images (ACIs) derived from the participants' data (folder: <strong>ACI_exp</strong>) and from the simulations (folder: <strong>ACI_sim</strong>). For each participant (or artificial listener) there are three ACIs (MAT files) for each of the corresponding noises. Cross predictions are also included with performance predictions across 'participants' (Crosspred.mat, 12 cross predictions for each noise) or across 'noises' (Crosspred-noise.mat, 3 cross predictions for each participant). The cross predictions all have the same names but are stored in dedicated directories.</li> </ul> <p><strong>Use these data:</strong></p> <ol> <li>Download all these data, place them in a local directory of your computer. If you have MATLAB and you downloaded a local copy of the fastACI toolbox (open access at: <a href="http://github.com/aosses-tue/fastACI">GitHub</a>) you can recreate the figures of our paper.</li> <li>After initialising the toolbox (type 'startup_fastACI;', without quotation marks in MATLAB) and then type either of the following commands, to recreate the figure you want. To recreate the figures in the main text:</li> </ol> <pre><code class="language-javascript">publ_osses2022b_JASA_figs('fig1','zenodo'); publ_osses2022b_JASA_figs('fig2a','zenodo'); publ_osses2022b_JASA_figs('fig2b','zenodo'); publ_osses2022b_JASA_figs('fig3','zenodo'); publ_osses2022b_JASA_figs('fig4','zenodo'); publ_osses2022b_JASA_figs('fig5','zenodo'); publ_osses2022b_JASA_figs('fig6','zenodo'); publ_osses2022b_JASA_figs('fig7','zenodo'); publ_osses2022b_JASA_figs('fig8','zenodo'); publ_osses2022b_JASA_figs('fig8b','zenodo'); publ_osses2022b_JASA_figs('fig9','zenodo'); publ_osses2022b_JASA_figs('fig9b','zenodo'); publ_osses2022b_JASA_figs('fig10','zenodo');</code></pre> <p>To generate the figures of the supplementary materials (Appendix in the BioRxiv preprint):</p> <pre><code class="language-javascript">publ_osses2022b_JASA_figs('fig1_suppl','zenodo'); publ_osses2022b_JASA_figs('fig2_suppl','zenodo'); publ_osses2022b_JASA_figs('fig3_suppl','zenodo'); publ_osses2022b_JASA_figs('fig3b_suppl','zenodo'); publ_osses2022b_JASA_figs('fig4_suppl','zenodo'); publ_osses2022b_JASA_figs('fig4b_suppl','zenodo'); publ_osses2022b_JASA_figs('fig5_suppl','zenodo'); publ_osses2022b_JASA_figs('fig5b_suppl','zenodo');</code></pre> <p><strong>References:</strong></p> <ul> <li><strong>Preprint</strong>: Alejandro Osses, Léo Varnet. "A microscopic investigation of the effect of random envelope fluctuations on phoneme-in-noise perception." BioRxiv.</li> <li><strong>fastACI toolbox</strong>: Alejandro Osses, Léo Varnet. fastACI toolbox: the MATLAB toolbox for investigating auditory perception using reverse correlation (v1.2). Zenodo. doi:<a href="https://doi.org/10.5281/zenodo.7314014">10.5281/zenodo.7314014</a>. Supplement to: <a href="http://github.com/aosses-tue/fastACI/tree/v1.2">https://github.com/aosses-tue/fastACI/tree/v1.2</a></li> </ul>
Dataset: "Traffic Noise at Moderate Levels Affects Cognitive Performance: Do Distance-Induced Temporal Changes Matter?"
<p>This repository contains the dataset presented in "Traffic Noise at Moderate Levels Affects Cognitive Performance: Do Distance-Induced Temporal Changes Matter?" (https://doi.org/10.3390/ijerph20053798) as well as the SPSS syntax used for the statistical evaluation. Additionally, calibrated binaural recordings of the evaluated stimuli are provided as 32 bit .wav files, the values stored in those files correspond to pascals.</p>
Experimental data of "High-field 1/f noise in hBN-encapsulated graphene transistors"
<p>Current-to-voltage characteristics along with flicker noise amplitude (A factor, description given in the paper) of the devices studied in the main and supplementary text of the article " by A. Schmitt et al. Dimensions of devices are provided in the article</p>
Noise Reduction by NBS in Valladolid city
<p>Noise pollution by traffic, construction works, etc. is a common city problem. Nuisance from noise is detrimental to neighbourhood liveability, living comfort and work environments, and can increase risk of serious health problems such as hearing loss and cardiovascular disease.</p> <p>Urban ecosystems provide noise reduction services by serving as a natural sound buffer. Vegetation provides both a direct and an indirect barrier to environmental noise. Starting with its direct functions, green belts attenuate noise by absorption, dispersal, and destructive interference of sound waves, though sound levels can intensify locally if measured right below tree crowns. Indirect noise reduction effects are generated by lessened wind speeds and the absorptive capacity of pervious soils. UGS also proved to offer noise reducing services via psychological effects: just observing the presence of a green wall can lead people to perceive less noise nuisance or alter the perception of noise as sounds such as flowing water, bird singing, and leaves rustling in the wind mask disturbing background noise.</p> <p>On the other hand, the methodology proposed for this KPI is based and uses the methodology and tools proposed by the European Commission Working Group Assessment of Exposure to Noise (WG-AEN).</p> <p>The Environmental Noise Directive (END) requires two main indicators to be applied in the assessment and management of environmental noise. The first indicator (Lden) is the noise level for the day, evening and night periods and is designed to measure ‘annoyance’. The END defines an Lden threshold of 55 dB. The second indicator (Lnight) is the noise level for night-time periods and is designed to assess sleep disturbance. The END defines an Lnight threshold of 50 dB. Member States must report the numbers of people who are exposed to noise levels above both thresholds for each noise source (e.g. roads, railways, airports, industry).</p>
Perceptual space of tire noises
<p>Data of a free sorting experiment of vehicle passing-by noises (70 km/h). Experiment conducted in the framework of Leon-T project, WP4 (see https://www.leont-project.eu/).</p>
Data for "Noise-induced servo errors in optical clocks utilizing Rabi interrogation"
<p>Numerical simulation data used for figures in "Noise-induced servo errors in optical clocks utilizing Rabi interrogation" (Metrologia, DOI 10.1088/1681-7575/acdfd4). For some figures, also the analytical results are given. For description of data, see header rows. For details, see the corresponding figure captions in the article.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.