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1,524 results for “Acoustics”
Dataset from: Correlation between proprioception, functionality, patient-reported knee condition and joint acoustic emissions
<p>Measures of functionality, proprioception, self reported status and joint acoustic emissions (AE) were recorded for a sample of general population. Specifically, threshold to detect passive motion (TTDPM), Knee Osteoarthritis Outcome Scores (KOOS) and 5 times sit-to-stand test (5STS) were collected from 51 participant. Knee AE were recorded using two sensors in different frequency ranges and three modes of AE event detection were investigated during cycling with 30 and 60 rpm cadences.</p>
Acoustic- and Moth sampling at Etonbury Wood (Bedford) - United Kingdom
<p>Moths sampling by hand of led buckets (https://www.vlinderstichting.nl/wat-wij-doen/meetnetten/meetnet-nachtvlinders/ledemmers/) and acoustic sampling by hand of AudioMoths (https://www.openacousticdevices.info/audiomoth) in a silvoarable system</p>
Temporal study of Santa Cruz Mountain bats using environmental DNA and acoustic data
<p>Data and R scripts for a study of niche partitioning in a bat community in California's Santa Cruz Mountains using environmental DNA and bioacoustic data collected over a roosting season.</p> <p>Associated with the publication "Temporal study of environmental DNA and acoustic data reveals coexistence of sympatric bat species in a North American ecosystem" in <em>Environmental DNA. </em></p>
Robust Acoustic Reflector Localization for Robots
<p>In this repository, we share our MATLAB code and dataset used to perform the experiments listed within our paper "<strong>Robust Acoustic Reflector Localization for Robots.</strong>"</p>
The Acoustics of Ely Cathedral's Lady Chapel: a study of its changes throughout history
<p>This repository contains the data set related to the conference paper “The Acoustics of Ely Cathedral’s Lady Chapel: a study of its changes throughout history”, published in the "I3DA 2021 International Conference" and available at: DOI: tbc</p> <p>This dataset contains the B-format Room Impulse Responses (RIR) in the Waveform Audio File standard Format (.wav) measured and simulated at a number of source-receiver combinations in the Ely Cathedral's Lady Chapel, used for the acoustical analysis performed as part of the CATHEDRAL ACOUSTICS project (CA-MRIR-EC-LC and CA-SRIR-EC-LC folders respectively).</p> <p>LICENCE.txt, METADATA.txt and README.txt contain a brief description of the folder contents, authors, and other useful information.</p> <p>Details on the acoustic measurement campaigns and acoustic simulations can be found in the conference paper.</p> <p>Please cite both the conference paper and the dataset if used.</p> <p>--------------------------------------------------</p> <p>This work is license under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). (see https://creativecommons.org/licenses/by-nc-sa/4.0/)</p> <p>--------------------------------------------------</p> <p>Dataset curated by Lidia Álvarez-Morales, Theatre, Film, Television and Interactive Media Department, University of York.<br> Contact: lidiaalvarezmorales@gmail.com, lidia.alvarezmorales@york.ac.uk.</p> <p>-------------------------------------------------</p> <p>Funding was provided by the European Union’s Horizon 2020 research and innovation programme (http://dx.doi.org/10.13039/501100007601) under the Marie Sklodowska-Curie grant agreement No 797586</p>
Supplementary material: Experimental acoustic characterisation of an endoskeletal antibubble contrast agent: first results
<p>Raw data connected to the journal article: </p> <p>Anastasiia Panfilova, Peiran Chen, Ruud J.G. van Sloun, Hessel Wijkstra, Michiel Postema, Albert T. Poortinga, Massimo Mischi. Experimental acoustic characterisation of an endoskeletal antibubble contrast agent: first results. <em>Medical Physics</em>, American Association of Physicists in Medicine. Volume 48, Issue 11, November 2021, Pages 6765-6780.</p>
The IHA database of human geometries including torso, head and complete outer ears for acoustic research
<p>This is the first version of the IHA database, which is being created in the project HAPPAA C1 funded by the Deutsche Forschungsgemeinschaft (DFG) – Projektnummer 352015383 - SFB 1330 C1. (https://uol.de/en/sfb-1330-hearing-acoustics)</p> <p>The database includes a subsample of 10 human geometries comprising the torso, head and the entire outer ear including the ear canal and eardrum. The data are available in two different 3D object formats: ply binary file, stl binary file.</p>
Geophysical and physical oceanography data and acoustic facies mapping results of the Central Basin in the northwestern Ross Sea margin, Antarctica
<p>Multi-channel seismic (MCS), sub-bottom profiler (SBP), multi-beam echosounder (MBES) and expandable conductivity-temperature-depth (XCTD) data and acoustic facies mapping results of the Central Basin in the northwestern Ross Sea margin, Antarctica. The Coordinate Reference System (CRS) for the MCS, SBP, MBES data and acoustic mapping results is WGS 84 / Antarctic Polar Stereographic (EPSG:3031). ). The geophysical data (MCS, SBP, MBES) and oceanographic measurements (XCTD) collected by the RV <em>Araon</em> are provided by the Korea Polar Data Center (<a href="https://kpdc.kopri.re.kr">https://kpdc.kopri.re.kr</a>).</p>
DATASET From the Reef to the Ocean: Revealing the Acoustic Range of the Biophony of a Coral Reef (Moorea Island, French Polynesia)
<p>Dataset corresponding to the article "From the Reef to the Ocean: Revealing the Acoustic Range of the Biophony of a Coral Reef (Moorea Island, French Polynesia)". 90 sites were recorded from the reef crest to 10 km in the open ocean off Moorea Island (French Polynesia) in 2016. Recordings were realized with drifting antennas made of a floater and an autonomous recorder EA-SDA14 (RTSys®, Caudan, France) connected to a wideband low-noise hydrophone HTI-92 (High Tech Inc., Long Beach, MS, USA) with a sensitivity of −155 ± 3 dB re 1 V µPa−1 and a flat frequency response from 2 Hz to 50 kHz.</p>
Creating Safe Environments: Optimal Acoustic Alarming of Laypeople in Fire Prevention - Online Supplement
<p>This online supplement contains datasets (raw data) and study material collected in an experimental study by the University of Münster, Germany. The study is part of a larger research project (<a href="https://www.brawa.ovgu.de/en/">https://www.brawa.ovgu.de/en/</a>) and examined the perception of acoustic fire alarm signals.</p> <p>Hazards like fires occur regularly and can cost people’s lives. Optimal auditory alarm signals enable laypeople to recognize dangers and to protect themselves. Existing fire alarm sound research focuses on alarm sounds and voice alerts presented singularly. We explored a combination of both and aimed to identify alarm signals that work optimally in everyday life. Thus, we conducted two online experiments: In Study 1 (<em>N</em> = 379), we tested eight alarm sounds regarding their typicality, their familiarity, their arousal, their valence, and their dominance. Siren-like alarm sounds were most effective. In Study 2 (<em>N</em> = 206), we combined the four most effective alarm sounds with a voice alert. The voice alert reinforced ambiguity reduction, action motivation, and action intention. Hence, we suggest using alarm sounds with siren-like patterns. They should be combined with a voice alert to foster a quick and specific (target task-oriented) reaction.</p> <p>The ethics committee of the University of Münster approved the studies (ID 2021-57-MT), and we preregistered both studies with AsPredicted.org under numbers #77031 and #81137 (see <a href="https://aspredicted.org/vx2rr.pdf">https://aspredicted.org/vx2rr.pdf</a> and <a href="https://aspredicted.org/mt9g3.pdf">https://aspredicted.org/mt9g3.pdf</a>). The studies were supported by the German Federal Ministry of Education and Research (grant numbers 13N15416 and 13N15419).</p> <p><strong>This online supplement includes: </strong></p> <ul> <li>Two codebooks describing all instructions and items in Study 1 and in Study 2</li> <li>Raw data (anonymized) and analysis scripts (Note: The raw data contains only the information of persons who were included in the analysis and who gave their informed consent. Some demographic information was deleted to ensure anonymity.)</li> <li>Study material: <ul> <li>Alarm signal example</li> <li>Hearing test implemented in both studies</li> </ul> </li> </ul>
Acoustic recording under landfast sea ice near glacier
<p>A hydrophone was deployed in February 2022 underneath landfast sea ice in Tempelfjorden, Svalbard. The hydrophone was located approximately 2 km from the glacier. Several major events were recorded by vibrations sensors on the ice next to the hydrophone. Source triangulation identified that the events were coming from the glacier wall. This dataset includes the recording of one of the events (with the event starting at about 0:14), and a sample thereof.</p>
Data from: Non-invasive Assessment of Cartilage Damage of the Human Knee using Acoustic Emission Monitoring: a Pilot Cadaver Study
<p>This dataset accompanies the following article: "Non-invasive Assessment of Cartilage Damage of the Human Knee using Acoustic Emission Monitoring: a Pilot Cadaver Study," in <em>IEEE Transactions on Biomedical Engineering</em>, doi: 10.1109/TBME.2023.3263388.</p> <p>Knee acoustic emissions (AE) recorded in the 100-450 kHz and 15-200kHz frequency ranges from a cadaver specimen knee in flexion/extension. Four stages of artificially inflicted cartilage damage and two sensor positions were investigated. </p> <p><em><strong>Stages of artificially inflicted cartilage damage:</strong></em> the cartilage surface damage on the medial compartment, KL III; the cartilage surface damage on the medial compartment plus patellofemoral surface, KL III; the cartilage surface damage on the medial compartment plus on the patellofemoral surface KL IV; the cartilage surface damage on the medial compartment plus on the patellofemoral surface and lateral compartment.</p> <p><strong><em>Sensor positions</em></strong>: medial and lateral knee</p>
InsectSet47 & InsectSet66: Expanded datasets for automatic acoustic identification of insects (Orthoptera and Cicadidae)
<p><strong>Updated full version with training, validation and test sets.</strong></p> <p>Two newly compiled datasets for training neural networks to automatically identify insect species while comparing adaptive, waveform-based frontends to conventional mel-spectrogram frontends for audio feature extraction. This work was <a href="https://doi.org/10.1371/journal.pcbi.1011541">published in PLOS</a> Computational Biology and the machine learning implementations were published on <a href="https://github.com/mariusfaiss/InsectSet47-InsectSet66-Adaptive-Representations-of-Sound-for-Automatic-Insect-Recognition">Github</a>.</p> <p>These datasets expand on the previously published <a href="https://doi.org/10.5281/zenodo.7072196">InsectSet32</a> by including recently published collections of insect recordings by citizen scientists from around the world. Recordings from <a href="https://bio.acousti.ca/">BioAcoustica</a>, <a href="http://xeno-canto.org/">xeno-canto</a> and <a href="http://inaturalist.org/">iNaturalist</a>, as well as private collections by <a href="https://orcid.org/0000-0002-8929-2737">Baudewijn Odé</a> were downloaded and manually inspected. Files with strong noise interference or intense filtering, as well as files containing sounds of multiple species were removed to compile these datasets. The files were standardised to 44.1 kHz mono WAV files ranging in length from less than one second to several minutes. Files containing long periods without insect sounds were edited into multiple smaller files with silent periods no longer than 5 seconds. These files are marked as edits in the annotation file and should be assigned together into train/validation/test sets to prevent data leakage. The annotation files contain information for each recording, including the file name, species name and identifier, as well as the data subset they were included in for training the neural network (training, test, validation).</p> <p>InsectSet47 expands on <a href="https://doi.org/10.5281/zenodo.7072196">InsectSet32</a> with recordings from <a href="http://xeno-canto.org/">xeno-canto</a> and contains 1006 original recordings from 47 species, with at least ten files per species. The total length of InsectSet47 is 22 hours. InsectSet66 further expands on InsectSet47 by adding research-grade audio observations from <a href="http://inaturalist.org/">iNaturalist</a>, with a total of 1554 recordings from 66 species, a total length of over 24 hours and a minimum of ten files per species.</p> <p>The datasets were split into the training, validation and test sets while ensuring a roughly equal distribution of audio files and audio material for every species in all three subsets. This resulted in a 60/20/20 split (train/validation/test) by file number and a 64/19.5/16.5 split by file length.</p>
Airborne Infrasound Data from The AtmoSOFAR Channel: First Direct Observations of an Elevated Acoustic Duct
<p>Airborne infrasound data including waveform recordings from two payloads attached to a single 6 m heliotrope that was launched at dawn (~0700 local) out of Belen Regional Airport, NM, USA. Balloon trajectory is also included. This data accompanies the publication titled, "The AtmoSOFAR Channel: First Direct Observations of an Elevated Acoustic Duct" submitted to Earth & Space Science.</p>
Acoustic Data for Endotracheal Intubation Simulation with Machine Learning Feedback
<p>This dataset contains raw acoustic data collected during endotracheal intubation simulations, utilized for developing a machine learning-based performance feedback system. The data includes .wav audio recordings sampled at 192 kHz, organized by buzzer and microphone location and intubation states.</p><p>The data is associated with the following paper:</p><p>Steffensen, T. L., Bartnes, B., Fuglstad, M. L., Auflem, M., & Steinert, M. (2023). Playing the pipes: Acoustic sensing and machine learning for performance feedback during endotracheal intubation simulation. <i>Frontiers in Robotics and AI</i>, <i>10–2023</i>. https://doi.org/10.3389/frobt.2023.1218174</p>
Calling activity of Birds in the White Mountain National Forest: Manifest of 99,778 acoustic recordings from bird plots in the Hubbard Brook Forest: 2016 - 2023
During 2016 - 2023, during the bird breeding season, we collected 99,778 files of bioacoustic recordings in and near the Hubbard Brook Experimental Forest in New Hampshire. Here, we provide a manifest of the sound files. Most files are one-hour recordings collected at 32 kHz and saved in FLAC format (~ 25 MB per file, ~ 13 TB total). Typical recording configuration was 05:00 - 08:00 and 17:30 - 20:30 local time. The full sound files have been saved in three respositories: two copies at Dartmouth College (Ayres lab) and one copy at the Macauley Library, Cornell Laboratory of Ornithology. The full sound files are available upon request. The file attributes within the manifest include date, start time, and recorder group: e.g., Main, 10ha, Oven, VW, AshBirch, and Ridge. Each recorder group had 5 - 20 recorders at plots separated by >100 m. Coordinates of each recorder are associated with plot names within metadata. The bird species expected to occur in these recordings are those from Holmes et al. (2021). These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. Holmes, R., S. Sillett, and M. Hallworth. 2021. Bird species recorded within the Hubbard Brook Experimental Forest and vicinity (1963-2020; updated January 2021). ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/da6cbb1ed8142d52a9d72762983742d8 (Accessed 2024-10-24).
Counts of tagged striped bass at forty sites throughout Plum Island estuary conducted July-October 2009 using acoustic telemetry.
Manual survey data was collected to measure striped bass distribution in Plum Island Estuary during the time period that they are in New England during their summer foraging migration. Acoustic telemetry was used to tag and track individual fish and provide measures of abundance at sample sites distributed throughout the estuary.
FIG. 1 in Panoploscelis scudderi Beier, 1950 and Gnathoclita vorax (Stoll, 1813): two katydids with unusual acoustic, reproductive and defense behaviors (Orthoptera, Pseudophyllinae)
FIG. 1. — Variations in characters used to distinguish Panoploscelis scudderi Beier, 1950 and Panoploscelis angusticauda Beier 1950 n. syn. females. The speci- mens pictured are breed from samples collected in Mitaraka (F1 and F2 generations). The frequent-most condition is represented in the left panels (A, E, J). Specimens from Mitaraka display variations in: A-D, the number of tubercle-bearing veins; E-I, the shape of ovipositor in side view (holotype of P. angusticauda display the same shape of ovipositor as illustrated in F); J-N, the shape of epiproct hind margin (male juvenile holotype of P. scudderi display the same shape of epiproct hind margin as illustrated in N; in P. angusticauda, the hind margin is somehow as in K). Scale bars: 10 mm.
Fig. 3 in New acoustic and molecular data shed light on the poorly known Amazonian frog Adenomera simonstuarti (Leptodactylidae): implications for distribution and conservation
Fig. 3. Preserved male of nominal Adenomera simonstuarti (Angulo & Icochea, 2010) (= genetic lineage 3): call voucher INPA-H 40967 (SVL = 23.4 mm) from the upper Juruá River, in Tarauacá, Brazilian state of Acre. This specimen corresponds to a call voucher (see Fig. 5). A−B. Body in dorsal and ventral views, not to scale. C−D. Detail of the ventral surface of right foot and hand, respectively. Note the nearly solid, dark-colored stripe along the underside of the forearm. Photographs by J. Magnusson. Scale bar = 5 mm.
Spherical Headgear HRIR Compilation of the Neumann KU100 and the Head acoustics HMS II.3
<p>[1] C. Pörschmann, J. M. Arend, and R. Gillioz, “How wearing headgear affects measured head-related transfer functions,” in <em>Proceedings of the EAA Spatial Audio Signal Processing Symposium</em>, 2019, pp. 49–54.<br> DOI link: <a href="https://doi.org/10.25836/sasp.2019.27">https://doi.org/10.25836/sasp.2019.27</a></p> <p>Files also available at in SOFA file format at <a href="http://sofacoustics.org/data/database/thk/">sofacoustics.org/data/database/thk/</a></p> <p>_______________________________________________________________________________________________________</p> <p>The spatial representation of sound sources is an essential element of virtual acoustic environments (VAEs). When determining the sound incidence direction, the human auditory system evaluates monaural and binaural cues, which are caused by the shape of the pinna and the head. While spectral information is the most important cue for elevation of a sound source, we use differences between the signals reaching the left and the right ear for lateral localization. These binaural differences manifest in interaural time differences (ITDs) and interaural level differences (ILDs). In many headphone-based VAEs, head-related transfer functions (HRTFs) are used to describe the sound incidence from a source to the left and right ear, thus integrating both monaural and the binaural cues. Specific aspects, like for example the individual shape of the head and the outer ears (e.g. Bomhardt, 2017), of the torso (Brinkmann et al., 2015), and probably even of headgear (Wersenyi, 2005; Wersenyi, 2017) influence the HRTFs and thus probably as well localization and other perceptual attributes. Generally speaking, spatial cues are modified by headgear, for example by wearing a baseball cap, a bicycle helmet, or a head-mounted display, which nowadays is often used in VR applications. In many real life situations, however, a good localization performance is important when wearing such items, e.g. in order to determine approaching vehicles when cycling. Furthermore, when performing psychoacoustic experiments in mixed-reality applications using head-mounted displays, the influence of the head-mounted display on the HRTFs must be considered. Effects of an HTC Vive head-mounted display on localization performance have already been shown in Ahrens et al. (2018). To analyze the influence of headgear for varying directions of incidence, measurements of HRTFs on a dense spherical sampling grid are required. However, HRTF measurements of a dummy head with various headgear are still rare, and to our knowledge only one dataset measured for an HTC Vice on a sparse grid with 64 positions is freely accessible (Ahrens, 2018). This work presents high-density measurement data of HRTFs from a Neumann KU100 and a HEAD acoustics HMS II.3 dummy head, either equipped with a bicycle helmet, a baseball cap, an Oculus Rift head-mounted display, or a set of extra-aural AKG K1000 headphones. For the measurements, we used the VariSphear measurement system (Bernschütz, 2010), allowing precise positioning of the dummy head at the spatial sampling positions. The various HRTF sets were captured on a full spherical Lebedev grid with 2702 points. In our study, we analyze the measured datasets in terms of their spectrum, their binaural cues, and regarding their localization performance based on localization models, and compare the results to reference measurements of the dummy heads without headgear. The results show that differences to the reference without headgear vary significantly depending on the type of the headgear. Regarding the ITDs and ILDs, the analysis reveals the highest influences for the AKG K1000. While for the Oculus Rift head-mounted display, the ITDs and ILDs are mainly affected for frontal directions, only a very weak influence of the bicycle helmet and the baseball cap on ITDs and ILDs was observed. For the spectral differences to the reference the results show maximal deviations for the AKG K1000, the lowest for the Oculus Rift and the baseball cap. Furthermore, we analyzed for which incidence directions the spectrum is influenced most by the headgears. For the Oculus Rift and the baseball cap, the strongest deviations were found for contralateral sound incidence. For the bicycle helmet, the directions mostly affected are as well contralateral, but shifted upwards in elevation. Finally, the AKG K1000 headphones generally has the highest influence on the measured HRTFs, which becomes maximal for sound incidence from behind. The results of this study are relevant for applications where headgears are worn and localization or other aspects of spatial hearing are considered. This could be the case, for example in mixed-reality applications where natural sound sources are presented while the listener is wearing a head-mounted display, or when investigating localization performance in certain situations, e.g. in sports activities where headgears are used. However, it is an important intention of this study to provide a freely available database of HRTF sets which is well suited for auralization purposes and which allows to further investigate the influence of headgear on auditory perception. The HRTF sets will be publicly available in the SOFA format under a Creative Commons CC BY-SA 4.0 license.</p> <p>________________________________________________________________________________________________________</p> <p><strong>Contact:</strong><br> Christoph Pörschmann<br> TH Köln - University of Applied Sciences<br> Institute of Communications Engineering<br> Department of Acoustics and Audio Signal Processing<br> Betzdorfer Str. 2, D-50679 Cologne, Germany<br> <a href="https://www.th-koeln.de/akustik">https://www.th-koeln.de/akustik</a></p> <p>_________________________________________________________________</p> <p> </p> <p> </p> <p> </p>
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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.