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952 results for “Noise”
Anthropogenic noise and light alter temporal but not spatial breeding behavior in a wild frog
<p><span>Increasing urbanization has led to large scale land-use changes, exposing persistent populations to drastically altered environments. Sensory pollutants, including low-frequency anthropogenic noise and artificial light at night (ALAN), are typically associated with urban environments and known to impact animal populations in a variety of ways. Both ALAN and anthropogenic noise can alter behavioral and physiological processes important for survival and reproduction, including communication and circadian rhythms. Although noise and light pollution typically co-occur in urbanized areas, few studies have addressed their combined impact on species' behavior. Here we assessed how anthropogenic noise and ALAN can influence spatial and temporal variation in breeding activity of a wild frog population. By exposing artificial breeding sites inside a tropical rainforest to multiple sensory environments, we found that both anthropogenic noise and ALAN impact breeding behavior of túngara frogs (<em>Engystomops pustulosus</em>), albeit in different ways. Males arrived later in the night at their breeding sites in response to anthropogenic noise. ALAN, on the other hand, led to an increase in calling effort. We found no evidence that noise or light pollution either attracted frogs to or repelled frogs from</span> <span>breeding sites. Thus, anthropogenic noise may negatively affect calling males by shifting the timing of sexual signaling. Conversely, ALAN may increase the attractiveness of calling males. These changes in breeding behavior highlight the complex ways that urban multisensory pollution can influence behavior and suggest that such changes may have important ecological implications for the wildlife that are becoming increasingly exposed to urban multisensory pollution.</span></p>
Supplementary audio files: Propagation effects in the synthesis of wind turbine noise
<p>The audio files are supplementary files required for the audio article titled: "Propagation effects in the synthesis of wind turbine noise". Each audio file refers to a signal of a test obtained from the wind turbine noise model which is described in the article. </p> <p>The audio files can be read as: NNx-x-TETIN-eeeeeee.mp3</p> <p>NNx-x refers to the Test case and case number for the syntheisized trailing edge and turbulent inflow noise, eeeeeee is the description of the specific case.<br> (tauTT- angle of the receiver, ff - for free field, GPE for ground and propagation effects included, GPE_Turb for ground and propagation effects included with turbulence scattering)</p> <p>eg: C2-2-TETIN_tau80_GPE_Turb.mp3 is the synthesized sound for Test case C2-2 in the article which includes the ground and propagation effects and also scattering due to turbulence. The receiver is at an angle of 80° with respect to the wind direction. </p>
Seismic noise interferometry and Distributed Acoustic Sensing (DAS): Inverting for the firn layer S-velocity structure on Rutford Ice Stream, Antarctica
<p>This dataset contains files including continuous DAS and geophone data and a refracted P wave travel time data collected on Rutford Ice Stream, Antarctica. The seismic data is used to perform seismic noise interferometry. The travel time data is used to perform refraction inversion to get the P wave velocity profile.</p> <p><br> 1. 7 hours of continuous DAS data (100 Hz sampling): 2020-01-14T00:00:19.598000Zoffset_****.mseed, with offset referring to the distance from the DAS channel to the interrogator.</p> <p>2. Corresponding 7 hours of vertical component continuous geophone (A000, located at DAS channel offset 570 m) data.</p> <p>3. Refraction P wave travel time from a geophone array refraction survey.</p>
Suppression of 1/f noise in graphene due to non-scalar mobility fluctuations induced by impurity motion
<p>Experimental dataset for article ''Suppression of 1/f noise in graphene due to non-scalar mobility fluctuations induced by impurity motion'', <a href="https://doi.org/10.48550/arXiv.2112.11933">arXiv:2112.11933</a></p>
Air quality and noise data in Santo Domingo and Santiago de los Caballeros, Dominican Republic from May 18,2020 to January 26, 2021
<p>Air and noise pollution affect the quality of life of any community. The data presented observes the noise level, eight air quality parameters and three weather parameters. The air quality parameters are carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3), nitrogen dioxide (NO2); three particle-matter variables: ultrafine particulate matter (PM1), fine particulate matter (PM2.5), and coarse particulate matter (PM10). The weather parameters are temperature, relative humidity and atmospheric pressure. The empirical measurements were collected every ten or twenty minutes in two cities of the Dominican Republic from May 18th, 2020 to January 26th, 2021. The data can provide insight on the changes in air quality and noise level and can be used to compare with other variables such as traffic conditions. </p>
DeLTA (Deep Learning Techniques for noise Annoyance detection) Dataset
<p>The Deep Learning Techniques for noise Annoyance detection (DeLTA) dataset comprises 2,980 15-second binaural audio recordings collected in urban public spaces across London, Venice, Granada, and Groningen (sourced from <a href="https://doi.org/10.5281/zenodo.5578572">International Soundscape Database</a>). A remote listening experiment was designed and hosted on Gorilla Experiment Builder, a professional online platform used for studying complex behaviours. The survey was then distributed via Prolific to a pool of pre-registered participants (N=1,221), and data collected between July 5th and July 23rd, 2021.</p> <p>During the listening experiment, participants listened to ten 15-second-long binaural recordings of urban environments and were instructed to select all the sound sources they could identify within the recording and then to provide an annoyance rating (from 1 to 10). For the sound source recognition task, participants were provided with a list of 24 labels they could select from. To collapse these into a single set of sound sources per recording, a “consensus” approach was considered, i.e., if two or more participants identified a source as being present in a recording, this source was considered to be effectively present. This resulted in a 2890 by 24 data frame (2890 recordings, each with up to 23 possible labels present and an average annoyance rating). On average, each recording has 3.2 identified sound sources present.</p> <p>Due to the constraints of the online survey software, Mp3 files were used for the listening experiment. Higher quality 24- or 32-bit 48kHz WAV files can be made available from the authors upon request. Each binaural audio recording consists of a 2 channel Mp3 file.</p>
D7.3 Dataset of noise produced by Hog-Jaeren wind park (simulation)
<p>This dataset was used in Deliverable 7.3 of Upwards.</p> <p>For further explanations refer to Work Package 4.</p> <p>Contained are results of the noise simulation for the Hog-Jaeren wind park with south-east wind direction.</p> <p>The yaw misalignment of the turbines in the freestream was varied as indicated by the folder names. Here 270 corresponds to a yaw misalignment of 0° while 260 corresponds to -10°.</p> <p>There are several types of 2D plots available:</p> <ul> <li>OASPL: Shows the OASPL in dB(A) in the area of the park</li> <li>noise_regulation: Contains the OASPL in dB(A) in the neghborhood of the park as well as lines indicating distance and noise based regulations by the Norwegian government.</li> <li>annoyance_level: Based on the OASPL the percentage of people being annoyed by the noise is presented.</li> </ul> <p>The file 'OASPL_at_observer_in_dBA.txt' contains the coordinates of an observer as well as the OASPL at that point.</p> <p> </p>
Ocean ambient noise data on the Chukchi Plateau from August 2018 to October 2019
<p>The noise spectrum level under different sea ice conditions, and monthly and hourly ambient noise level on the Chukchi Plateau from August 1st, 2018 to October 31st, 2019.</p>
Supplementary audio files: Physics-based synthesis of wind turbine noise
<p>The supplementary files required in the thesis "Physics-based synthesis of wind turbine noise" for the "Chapter 3: Synthesis tool". <br> <br> A repository can also be found on: https://sites.google.com/view/david-mascarenhas</p> <p><br> For the test cases: Section 3.9 --------------------------------------------------<br> The audio files are supplementary files required for the audio article titled: "Propagation effects in the synthesis of wind turbine noise". Each audio file refers to a signal of a test obtained from the wind turbine noise model which is described in the article. <br> The audio files can be read as: NNx-x-TETIN-eeeeeee.mp3<br> NNx-x refers to the Test case and case number for the syntheisized trailing edge and turbulent inflow noise, eeeeeee is the description of the specific case.<br> (tauTT- angle of the receiver, ff - for free field, GPE for ground and propagation effects included, GPE_Turb for ground and propagation effects included with turbulence scattering)</p> <p>eg: C2-2-TETIN_tau80_GPE_Turb.mp3 is the synthesized sound for Test case C2-2 in the article which includes the ground and propagation effects and also scattering due to turbulence. The receiver is at an angle of 80° with respect to the wind direction. </p> <p> </p> <p> </p>
Correlation between noise and vegetation in urban areas
<p>Analysis of correlation between vegetation and noise in urban areas.</p> <p>DOI article: 10.22541/au.150651402.23622693</p> <p> </p>
High rates of vessel noise disrupt foraging in wild harbour porpoises (Phocoena phocoena) - scripts and example dataset
<p>This upload contains Matlab scripts used to compute third-octave levels from audio recorded with DTAG-3 tags on free-ranging harbour porpoises. It also contains examples of results, outputs of such scripts (hp12_272a_noisedata.mat and hp12_293a_noisedata.mat), for two of the seven animals in the study, as well as sensor data for all the animals (e.g. hp12_272a_prh625.nc). The metadata for all the uploaded data are stored in netCDF files (.nc) and the overview plots show noise, vessel presence and foraging data for all study animals. Finally, the upload contains scripts that use the results to perform a series of permutation tests to compare foraging buzz count and total buzz duration in minutes with high- and low-level noise.</p>
Data Archive for "Speckle Noise Reduction via Linewidth Broadening for Planetary Laser Reflectance Spectrometers"
<p>This archive contains the raw speckle images and experimental notes for the data contained in the journal article: "Speckle Noise Reduction via Linewidth Broadening for Planetary Laser Reflectance Spectrometers".</p> <p>The data available are:</p> <p>Raw speckle images for each of 5 illumination sources:</p> <ol> <li>Single-Frequence diode (files are named "IPS")</li> <li>Dual Mode pump diode (files are named "II-IV")</li> <li>Fabry Perot didoe (files are names "FP")</li> <li>Superluminescent Diode (files are named "SLD")</li> <li>Whilte light halogen source (files are named "WL")</li> </ol> <p>For each of theses source there are 5 images for each reflectance target, four speckle patterns and one background image with the laser source off. The naming convention is:</p> <p>LaserName_TargetReflectance_Target Rotation State or BKG.tiff</p> <p>For example, IPS_50_2 is the single-frequency laser using the 50% reflectance target and second rotation state of the target.</p> <p>The integration time for each image is give in the file "Experiment Paramters.csv" and was manually change to keep the maximum intensity at roughly 75% saturation.</p> <p>The spectra of the three laser sources presented in the manuscript are also included as .csv files</p>
Data for: Timescales of Autogenic Noise in River Bedform Evolution and Stratigraphy
<p>Bedforms are ubiquitous features on alluvial river channels. Bedform deposits—fluvial cross strata— are the fundamental sedimentary structures of the rock record on Earth and Mars. Bedform evolution and preserved cross strata respond to floods; however, it is unclear which flood durations are likely to be represented in bedform evolution and cross strata. To address this, we quantified the structure of autogenic noise in bedform evolution using high-resolution spatiotemporal data from a steady-state, physical experiment of bedform evolution. </p> <p>The data herein accompanies the manuscript "Timescales of Autogenic Noise in Bedform Evolution and Fluvial Cross Strata " by Vamsi Ganti, Madeline M. Kelley, Debsmita Das and Robert C. Mahon. In this manuscript, we quantified the scales of autogenic noise in sediment efflux, bedform evolution, and preserved deposition rates in fluvial cross strata. We accomplish this using a steady-state experiment of bedform evolution and perform spectral analysis of bed elevation, sediment efflux and preserved deposits. We find that bedform-group (quasi-stable collection of bedforms) turnover timescale sets the lower limit for detecting flood signals in bedform evolution, and floods with duration shorter than bedform turnover timescale can be severely degraded in bedform evolution and cross strata.</p>
Raw data for: Self-Referencing for Quasi Shot-Noise-Limited Widefield Transient Microscopy
<p>The repository shares the data used to produce the figures in the paper "Self-Referencing for Quasi Shot-Noise-Limited Widefield Transient Microscopy", submitted 2024 in Optics Express. </p> <p>The upload includes for each Figure the raw and processed self-referenced transient normalized transmission data, (T_on-T_off) / T_off, from which all results and statistics are deduced.</p>
Assessment of the spatial extent of local active noise control - dataset
<p>Experiment setup, data and supplementary scripts for the listening experiment "Assessment of the spatial extent of local active noise control" in Feb. 2023 at the Institute of Electronic Music and Acoustics in Graz, Austria</p> <p>A listening experiment has been conducted, trying to find the spatial extents of local active noise control (ANC). Participants were instructed to move their heads along the x-axis or rotate clock- and counterclockwise (yaw). A button had to be pressed if the perceived zone of comfort is left, and the current position was recorded. This task was repeated for different controlled bandwidths, signals and source directions. Additionally, scripts to generate the ANC filters, a brief statistical analysis and recordings at several positions for different stimuli/conditions are provided. Acoustic measures at the point of cancellation and at various other positions are further analyzed with and without ANC.</p>
Railway tram curving noise datasets
<p>This dataset is part of the data descriptor. The data are collected by recording the noise generated by the trams at the center of the curve alignment when the trams traverse along the curve alignment. The comprehensive dataset and analysis contribute valuable insights into tram curve noise, aiding urban planning and noise mitigation efforts.</p> <p>Note:</p> <p>Datasets: datasets.csv and wav_files.zip</p> <p>Python code: curve_noise.py</p> <p>Algorithm: curve_noise.ipynb</p> <p> </p>
Transfer function measurements for simulating environmental noise at hearable microphones
<p>This dataset is supplementary material to the conference paper "Multi-Microphone Noise Data Augmentation for DNN-based Own Voice Reconstruction for Hearables in Noisy Environments" presented at ICASSP 2024 [1].</p> <p>The dataset consists of impulse response measurements for 18 device users (5 female, 13 male) wearing hearable devices in both ears. <br>The dataset was recorded in a sound-proof listening room using the Hearpiece prototype device (closed vent variant) [2] with a sampling frequency of 44.1 kHz.<br>Impulse responses were measured with exponential sweeps from 80 Hz to 22.05 kHz with a duration of 3s played from 8 loudspeakers arranged in a circle of approximately 1.5m radius. <br>The loudspeakers were located in the horizontal plane around the device users in 45°-steps (azimuth), starting from 22.5° to the right (where 0° is the front from the device users' perspective).</p> <p>The measurements are contained in the folder <code>measurements</code>. Each subfolder contains measurements from a different device user (e.g., <code>VP_01</code>). <br>Each file contains the measurement for one direction, e.g. <code>VP_01/data_0.npz</code> contains the measurement of device user <code>VP_01</code> for 22.5° azimuth, <code>VP_01/data_1.npz</code> is the measurement for the same device user for 22.5°+45° and so on.<br>Measurements of device users where the device could not be inserted, or where the fit did not provide sufficient attenuation of external sounds to the in-ear microphone, were excluded.</p> <p>The impulse responses for two Hearpiece devices (closed vent), the concha and in-ear microphones were measured.<br>A DPA 6060 lavalier clip microphone and a Tbone SC140 cardiod microphone were also included in the measurement as reference channels. </p> <p>The channels of the measurements (counting from 0):</p> <p> 0: Lavalier-microphone clipped to the shirt neck, shirt collar etc. of the device user<br> 1: Reference microphone about 50 cm in front of the device user<br> 2: Left in-ear microphone Hearpiece<br> 3: Left concha microphone Hearpiece<br> 4: Right in-ear microphone Hearpiece<br> 5: Right concha microphone Hearpiece</p> <p><br>The measurement consists of impulse responses from the loudspeaker to the hearable device microphones and reference microphones, and corresponding transfer functions. <br>Measurement metadata is included as well. <br>The measurement files contain a python dictionary with the following fields:</p> <ul> <li><code>test_signal</code>: the signal used for playback, consisting of a pause, the sweep, and another pause</li> <li><code>rec_signal</code>: the recorded signal (sweep played from the loudspeaker, recorded at the microphones)</li> <li><code>sweep</code>: the generated exponential sweep signal without pauses</li> <li><code>T</code>: actual duration of the sweep (~2 Seconds)</li> <li><code>sweep_inv</code>: inverse sweep (inverse w.r.t convolution of the sweep with the system response)</li> <li><code>sweep_inv_spectrum</code>: spectrum of the inverse sweep</li> <li><code>f11</code>: the frequency (in Hz) corresponding to the <code>RampLen</code> of the fade-in at the beginning of the sweep</li> <li><code>T_desd</code>: desired duration of the sweep in seconds (2 Seconds)</li> <li><code>T_rec</code>: recording duration in seconds (3 Seconds)</li> <li><code>start_frequency</code>: Minimum frequency in the measurement / first frequency in the sweep (80 Hz)</li> <li><code>RampLen</code>: Length of the fade-in ramp applied to the beginning of the sweep (based on a Hanning window) (2048 Samples)</li> <li><code>pre_pause_len</code>: pause time between starting the measurement and sweep playback (88200 Samples)</li> <li><code>after_pause_len</code>: pause time after sweep playback (44100 Samples)</li> <li><code>n_repetitions</code>: Number of repetitions for the measurement (1)</li> <li><code>n_channels</code>: Number of recorded channels including loopback (7 = 4 Hearpiece, 2 reference, 1 loopback)</li> <li><code>coh_mat</code>: Mean Squared Coherence per channel (between the measured sweep and the playback sweep signal), has shape (frequencies up to <code>samplerate</code>/2 x channels)</li> <li><code>ir_loopback</code>: the measured impulse response of the loopback channel, used to measure and compensate system delay from audio interface</li> <li><code>ir_mic</code>: the measured impulse responses of the hearable and reference microphones, with shape (samples, channels)</li> <li><code>tf_mic</code>: the measured transfer functions between the loudspeaker and the hearable and reference microphones, with shape (frequencies up to <code>samplerate</code>/2, channels)</li> <li><code>system_delay</code>: the measured system delay from the audio interface (position of the peak of the correlation between playback sweep and loopback sweep signals)</li> <li><code>samplerate</code>: The sampling rate used for the measurements (44100 Hz)</li> </ul> <p>This dataset is compatible with the German own voice recordings available at <a href="../records/10844599" target="_blank" rel="noopener">https://zenodo.org/records/10844599</a> (same participants+device insertion and measurement setup).</p> <p>The example script <code>generate_indiv_noise_dataset.py</code> can be used to augment a single-channel noise dataset to obtain simulated individual hearable noise signals,<br>similar to [1] but using impulse responses directly as filters instead of first computing relative transfer functions and then applying them in the STFT domain.</p> <p><br>[1] M. Ohlenbusch, C. Rollwage, S. Doclo: "Multi-microphone Noise Data Augmentation for DNN-based Own Voice Reconstruction for Hearables in Noisy Environments". In: Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Seoul, South Korea, Apr. 2024, pp. 416-420.<br>[2] F. Denk, M. Lettau, H. Schepker, S. Doclo, R. Roden, M. Blau, J.-H. Bach, J. Wellmann, and B. Kollmeier: "A One-Size-Fits-All Earpiece with Multiple Microphones and Drivers for Hearing Device Research". In: Proc. AES International Conference on Headphone Technology. San Francisco, USA, Aug. 2019.</p>
Plasma Parameters From Wind Mission Radio Observations Using Quasi-Thermal Noise Spectroscopy
<p>Database of the high-resolution Velocity Diftribution Function (VDF) moments from Wind Thermal Noise Receiver (TNR) using Quasi-Thermal Noise (QTN) Spectroscopy</p>
I2i Noise Bioassay Datasets
Open the record for dataset details and reuse information.
Computational Artifacts for the Paper "Are Noise-resilient Logical Timers useful for Performance Analysis?"
<p>This repository contains computational artifacts for the paper "Are Noise-resilient Logical Timers useful for Performance Analysis?" to be submitted to <a href="https://sc-protools-workshop.github.io/protools24/">ProTools@SC24.</a></p> <p>See also the <a href="https://sc24.supercomputing.org/program/papers/reproducibility-initiative/">SC24 reproducibility initiative.</a></p> <p> </p> <p>Contains</p> <ul> <li>Source code of <a href="https://doi.org/10.5281/zenodo.10822140">Score-P </a>, including implementation of the logical clock algorithm from the paper</li> <li>Software to post-process the Cube files generated by measurements</li> <li>Benchmarks <ul> <li>Source code</li> <li>Configuration skripts</li> <li>Measurement results, including output logs, Cube files</li> <li>Post-processing skripts and results</li> </ul> </li> </ul> <p> </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.