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1,300 results for “Sounds”
Raw data for Figures 1-4 for journal article: "Transcutaneous and percutaneous bone conduction sound propagation in single-sided deaf patients and cadaveric human whole heads"
<p>This is a data set containing the raw data for figures 1-4 from the journal article:</p> <p>"Transcutaneous and percutaneous bone conduction sound propagation in single-sided deaf patients and cadaveric human whole heads"</p> <p>Original article DOI: 10.1080/14992027.2021.1903586</p> <p>Original article link: https://pubmed.ncbi.nlm.nih.gov/34097554/</p> <p> </p> <p>The data is contained within plots in word files, created with Microsofft Office (v18).</p>
Data from: Vocal signatures affected by population identity and environmental sound levels
<p>Passive acoustic monitoring has improved our understanding of vocalizing organisms in remote habitats and during all weather conditions. Many vocally active species are highly mobile, and their populations overlap. However, distinct vocalizations allow the tracking and discrimination of individuals or populations. Using signature whistles, the individually distinct calls of bottlenose dolphins, we calculated a minimum abundance of individuals, characterized and compared signature whistles from five locations, and determined reoccurrences of individuals throughout the Mid-Atlantic Bight and Chesapeake Bay, USA. We identified 1,888 signature whistles in which the duration, number of extrema, start, end, and minimum frequencies of signature whistles varied significantly by site. All characteristics of signature whistles were deemed important for determining from which site the whistle originated and due to the distinct signature whistle characteristics and lack of spatial mixing of the dolphins detected at the Offshore site, we suspect that these dolphins are of a different population than those at the Coastal and Bay sites. Signature whistles were also found to be shorter when sound levels were higher. Using only the passively recorded vocalizations of this marine top predator, we obtained information about its population and how it is affected by ambient sound levels, which will increase as offshore wind energy is developed. In this rapidly developing area, these calls offer critical management insights for this protected species.</p>
Nonlinear sound-sheet microscopy: imaging opaque organs at the capillary and cellular scale
<p>This dataset accompanies the article entitled: "Nonlinear sound-sheet microscopy: imaging opaque organs at the capillary and cellular scale".</p> <div> <h3><strong>Wells with E. Coli (Wells_EColi.zip)</strong></h3> <p><strong>Codes are hosted <a title="NSSM github repo" href="https://github.com/MarescaRenaudLabs/NSSM/releases/tag/v1.0.0" target="_blank" rel="noopener">here</a></strong></p> </div> <p>Representative dataset for paper figure 2D,E. The file <code>RFData_planes_figure_2E.mat</code> contains the RFData and the required parameters to reconstruct two orthogonal sound sheets, in SSM and NSSM mode. The file <code>demo_reconstruct_orthogonal_NSSM_images.m</code> shows how to reconstruct the sound sheet data.</p> <p>The file <code>beamformed_volume_figure_2E.mat</code> contains beamformed SSM and NSSM volumes as displayed in figure 2G. The file <code>demo_navigate_3D_NSSM_data.m</code> can be used to view the volumes.</p> <div> <h3><strong>mARG expression in orthopic tumors (mArg_tumors.zip)</strong></h3> <p><strong>Codes are hosted <a title="NSSM github repo" href="https://github.com/MarescaRenaudLabs/NSSM/releases/tag/v1.0.0" target="_blank" rel="noopener">here</a></strong></p> </div> <p>Representative dataset for paper figure 3B. The file <code>RFData_planes_figure_3B.mat</code> contains the RFData and the required parameters to reconstruct two orthogonal sound sheets, in SSM and NSSM mode. The file <code>demo_reconstruct_orthogonal_NSSM_images.m</code> shows how to reconstruct the sound sheet data.</p> <p>The file <code>beamformed_volume_figure_3B.mat</code> contains beamformed SSM and NSSM volumes as displayed in figure 3C. The file <code>demo_navigate_3D_NSSM_data.m</code> can be used to view the volumes.</p> <h3><strong>Nonlinear Soundsheet Localization Microscopy (NSSLM_zenodo_archive.zip)</strong></h3> <p><strong>Codes are hosted <a title="NSSLM github repo" href="https://github.com/MarescaRenaudLabs/NSSLM/releases/tag/v1.0.0" target="_blank" rel="noopener">here</a></strong></p> <p>A zip compressed dataset containing beamformed images and post-processed trajectories for Nonlinear Soundsheet Localization Microscopy.</p> <ul> <li>The files <code>ImgNSSM_00x.mat</code> are 4D data arrays of size (157x160x2x2100). They hold 2 soundsheets repeated for 2100 frames at 1000Hz (more details are provided in the Material and Methods section of accompanying papers). Use this dataset to run script <code>processNSSLM.m</code></li> <li>The file <code>Params.mat</code> is a file containing the <code>ULM</code> structure. This comprises the fields necessary to execute ULM codes from the <a href="https://github.com/AChavignon/PALA" target="_blank" rel="noopener">PALA toolbox</a></li> <li>A folder named Trajectories. This contains 50 files. Each contains the trajectories obtained from NSSLM processing of the entire sequence for soundsheet indexed 1 in the 4D matrix. Use this dataset to run script <code>renderingNSSLM.m</code></li> </ul> <p>Each of these datasets is to be used in conjunction with the different example scripts given on <strong><a title="NSSLM github repo" href="https://github.com/MarescaRenaudLabs/NSSLM/releases/tag/v1.0.0" target="_blank" rel="noopener">here</a></strong>.</p> <h3>Codes hosted on github</h3> <p>For accompanying codes, please refer to:</p> <ul> <li>https://github.com/MarescaRenaudLabs/NSSM/releases/tag/v1.0.0</li> <li>https://github.com/MarescaRenaudLabs/NSSLM/releases/tag/v1.0.0</li> </ul>
Data: Responsiveness and habituation to repeated sound exposures and pulse trains in blue mussels
<p>Data abstract:</p> <p>Time series data on the valve gape behaviour of blue mussels (<em>Mytilus edulis</em>) that were exposured to sound treatments. Here, we provide the valve gape (time series data expressed in proportion open) of all mussels over the course of their trial and the timing of the sound exposures.</p> <p> </p> <p>Paper abstract:</p> <p>Anthropogenic sound has been shown to affect marine animals across taxa. However, bivalves and other invertebrates received limited attention and most studies across taxa focussed on immediate, rather than long-term, effects of sound. Most bivalves adopt a sessile or sedentary lifestyle and are therefore expected to be exposed to the same sounds for long periods or repeatedly. For this reason, bivalves are an especially relevant taxonomic group to study long-term effects of sound. In the current study, we examined whether blue mussels (<em>Mytilus edulis</em>) habituate to repeated sound exposures and whether they recover quicker from a single pulse exposure than from a pulse train. We equipped individual mussels with sensors to monitor valve gape and exposed them to repeated sound playback. We found that mussels responded to sound by partially closing their valves. This response was consistent and repeatable, but decayed over sequential exposures to the same sound stimulus, and was stronger again with exposure to a different sound. This pattern is clear evidence for acoustic habituation in a bivalve. Additionally, we found no differences in the initial response and recovery (time to return to baseline levels) between mussels that were exposed to single pulses and pulse trains. Our results therefore show that mussels are able to habituate to sound and suggest that mussels mostly respond to the onset of a pulse train. Future research is needed to determine whether mussels also habituate in situ to actual anthropogenic sound and whether a lack of a behavioural response also implies that other negative effects are also absent.</p> <p> </p> <p>Paper reference:</p> <p>Hubert, J., Booms, E., Witbaard, R., Slabbekoorn, H. (2022). Responsiveness and habituation to repeated sound exposures and pulse trains in blue mussels. <em>Journal of Experimental Marine Biology and Ecology</em>. 547, 151668. DOI: 10.1016/j.jembe.2021.151668</p>
Identification of Ionospheric Acoustic Wave Signatures from Conventional Surface Explosions Using MF/HF Doppler Sounding
<p>These HDF5 files contain complex time series data from HF receptions of a Digisonde Portable Sounder 4D (DPS4D). Each data point is the phase and amplitude of a decoded Sky Map mode pulse. </p>
Data for "Second sound attenuation near quantum criticality"
<p>This dataset is for research article "Second sound attenuation near quantum criticality".</p>
Video of female wing and mandibular protest sounds of the katydid, Nesoecia nigrispina (Orthoptera, Pseudophyllinae)
<p>The katydids,<em> Nesoecia nigrispina</em> (Stal, 1873) (Orthoptera, Pseudophyllinae, Cocconotini) emit sound signals in three ways: using the tegminal sound apparatus (males only), wings, and, presumably, mandibles (individuals of both sexes). The repertoire of sound signals includes calling song of two types and 3 types of protest signals. Males, which, like other katydids, have tegminal stridulatory organ, produce sounds of all types. However, the emission of protest signals of the 2nd and 3rd types is carried out with the help of the wings and mouth organs (apparently, mandibles). Females can also produce Type 2 and Type 3 protest sounds in the same way as males.</p> <p>In the video, can be seen and heard how the female produces wing protest sounds, as well as short clicks that accompany the movements of the mouthparts.</p>
Video of male wing and mandibular protest sounds of the katydid, Nesoecia nigrispina (Orthoptera, Pseudophyllinae)
<p>The katydids,<em> Nesoecia nigrispina</em> (Stal, 1873) (Orthoptera, Pseudophyllinae, Cocconotini) emit sound signals in three ways: using the tegminal sound apparatus (males only), wings, and, presumably, mandibles (individuals of both sexes). The repertoire of sound signals includes calling song of two types and 3 types of protest signals. Males, which, like other katydids, have tegminal stridulatory organ, produce sounds of all types. However, the emission of protest signals of the 2nd and 3rd types is carried out with the help of the wings and mouth organs (apparently, mandibles). Females can also produce Type 2 and Type 3 protest sounds in the same way as males.</p> <p>In the video, can be seen and heard how the male produces wing protest sounds, as well as short clicks can be heard that accompany the movements of the mouthparts.</p>
Dízi Fingering Dataset: Acoustic Impedance and Audio Spectra, with Sound Files
<p>This database presents acoustic impedance spectra measured on a G-key Dízi (Chinese transverse flute): a total of 35 fingerings (both standard and alternative fingerings) for 24 semi-chromatic notes ranging from D5 to G7 (i.e. the instrument’s typical playing range), for both with the membrane attached and without. Accompanying each fingering entry is the sound recording and corresponding audio spectra.</p> <p> </p> <p>An interactive graphical version of this database is also presented at <a href="https://acoustics.sutd.edu.sg/dizi-impedance/">https://acoustics.sutd.edu.sg/dizi-impedance/</a> </p> <p> </p> <p>The impedance measurements (using the “<a href="https://apc01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fnewt.phys.unsw.edu.au%2Fjw%2Freprints%2FDickensSmithWolfe.pdf&data=04%7C01%7Cyixian_tan%40sutd.edu.sg%7C7763bf2bc6eb4f9c849908da09080e5d%7C3476b776e9904f72b95062489831623d%7C0%7C0%7C637832227461557470%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=AfSXOQEqxZxbz%2BPJ7ZnbtAuSZn6xF1YBBUmQenjRZmA%3D&reserved=0">three-microphone two-calibration</a>” technique) were conducted at the Acoustics Lab, School of Physics, UNSW, Sydney Australia. The authors are grateful to John Smith and Joe Wolfe for hosting us. The acoustic impedance measurement hardware and software was developed by Paul Dickens and we used a version of the software subsequently modified by Noel Hanna.</p>
VISIONS-1 rocket experiment: Sounding rocket observations in the auroral zone at night following a substorm.
<p>These data files support the Frontiers in Astrophysics and Space Sciences article by Rowland, D. E., et al. (2022) entitled “Imaging Low-Energy Ion Outflow in the Auroral Zone”.</p>
Drilling sound dataset
<p>In this dataset, sound from a drill machine in Valmet AB, Sundsvall, Sweden was recorded with four AudioBox iTwo Studio microphones. For capturing drill sounds, 96 kHz was used as the sampling rate. The dataset contains 134 sounds with lengths of 20.83ms and 41.67ms in two classes (normal and anomalous).</p>
Real-Life Indoor Sound Event Dataset (ReaLISED) for Sound Event Classification (SEC)
<p>The Real-Life Indoor Sound Event Dataset (ReaLISED) offers the scientific community the possibility of testing Sound Event Classification (SEC) algorithms with new real indoor audio event recordings. The full set is made up of 2479 sound recordings of 18 events. The 18 event classes are the following: beater, cooking, cupboard/wardrobe, dishwasher, door, drawer, furniture movement, microwave, object falling, smoke extractor, speech, switch, television, vacuum cleaner, walking, washing machine, water tap, and window. There are 2479 clips of isolated sounds, which result in 3624.51 seconds. The number of events in each class is between 104 for the "Window" class and 190 for the “Speech” class, with a mean value of 138 events and a standard deviation of 25.</p> <p>Four Olympus LS-100 recorders were used. The sampling frequency was set to 44.1 kHz and 24 bits per sample. The stereo mode was used, and a medium sensitivity of the microphone was set. The distance between the recorder and the sound source was set to approximately 30-40 cm.</p> <p>Apart from the labels related to the class of event, extra information for each recording is provided in order to be exploited if necessary in the future, with other research purposes. This extra information completes the description of the sound source.</p> <p>The dataset is introduced to the scientific community by providing all the .flac files which composed it. The name of the files is built with 5 pieces of information, separated with underscores (“_”), with the format “abc_123_45_67_8.flac′′:</p> <ul> <li> <p>“abc”: the first three letters indicate the source that produces the sound. This segment can take 18 different values: ‘bea’ (beater), ‘coo’ (cooking), ‘cup’ (cupboard/wardrobe), ‘dis’ (dishwasher), ‘doo’ (door), ‘dra’ (drawer), ‘fur’ (furniture movement), ‘mic’ (microwave), ‘obj’ (object falling), ‘smo’ (smoke extractor), ‘spe’ (speech), ‘swi’ (switch), ‘tel’ (television), ‘vac’ (vacuum cleaner), ‘wal’ (walking), ‘was’ (washing machine), ‘wat’ (water tap), win’ (window).</p> </li> <li> <p>“123”: this set of digits identifies the event among the number of events produced by the source identified with “abc”. This segment can take all the values between ‘001’ and ‘190’, which is the maximum number of events of a particular class we can find in the dataset (speech).</p> </li> <li> <p>“45”: this set of digits identifies the action that produce the sound. This segment can take 11 different values: ‘01’ (close), ‘02’ (open), ‘03’ (throw), ‘04’ (turn on), ‘05’ (turn off), ‘06’ (move), ‘07’ (plug), ‘08’ (unplug), ‘09’ (raise), ‘10’ (lower), and ‘00’ (there is no information about the action).</p> </li> <li> <p>“67”: this set of digits identifies the material the sound source is made of. This segment can take 14 different values: ’01’ (wood), ’02’ (glass), ’03’ (metal), ’04’ (plastic), ’05’ (ceramic), ’06’ (synthetic), ’07’ (cardboard), ’08’ (marble), ’09’ (floating platform), ’10' (platelet), ’11’ (wicker), ’12’ (carpet), ’13’ (medium-density fibreboard MDF), and ’00’ (there is no information about the material).</p> </li> <li> <p>“8”: the last digit gives approximate information about the intensity of the recorded sound. It can take 4 different values: ’1’ (low intensity), ’2’ (medium intensity), ’3’ (high intensity), ’0’ (there ir no information about the intensity).</p> <p>For clarity, some examples of audio file names with this code are shown hereunder:</p> </li> <li> <p>“doo_040_02_00_3.flac” is the name of the 40th file in the Door class, described as “opening a door of unknown material with high intensity”.</p> </li> <li> <p>“fur_058_06_01_2.flac” is the name of the 58th file in the furniture movement class, described as “moving a wooden furniture with medium intensity”.</p> </li> <li> <p>“vac_001_00_00_0.flac” is the name of the 1st audio file in the vacuum cleaner class, described as “using the vacuum cleaner, without information about the action, neither the material or the intensity”.</p> </li> </ul>
Motor performance in violin bowing: Effects of attentional focus on acoustical, physiological and physical parameters of a sound-producing action
<p>Violin bowing is a specialised sound-producing action, which may be affected by psychological performance techniques. In sport, attentional focus impacts motor performance, but limited evidence for this exists in music. We investigated the effects of attentional focus on acoustical, physiological, and physical parameters of violin bowing in experienced and novice violinists. Attentional focus significantly affected spectral centroid, bow contact point consistency, shoulder muscle activity, and novices’ violin sway. Performance was most improved when focusing on tactile sensations through the bow (somatic focus), compared to sound (external focus) or arm movement (internal focus). Implications for motor performance theory and pedagogy are discussed.</p>
Cortical adaptation to sound reverberation
<p>In almost every natural environment, sounds are reflected by nearby objects, producing many delayed and distorted copies of the original sound, known as reverberation. Our brains usually cope well with reverberation, allowing us to recognize sound sources regardless of their environments. In contrast, reverberation can cause severe difficulties for speech recognition algorithms and hearing-impaired people. The present study examines how the auditory system copes with reverberation. We trained a linear model to recover a rich set of natural, anechoic sounds from their simulated reverberant counterparts. The model neurons achieved this by extending the inhibitory component of their receptive filters for more reverberant spaces, and did so in a frequency-dependent manner. These predicted effects were observed in the responses of auditory cortical neurons of ferrets in the same simulated reverberant environments. Together, these results suggest that auditory cortical neurons adapt to reverberation by adjusting their filtering properties in a manner consistent with dereverberation.</p>
Sound Art and Sensorial Perception: A Practice-based Study
<p>Supplementary data as body of work for the PhD thesis:<br> <a href="https://researchrepository.ucd.ie/handle/10197/12924">Sound Art and Sensorial Perception: A Practice-based Study</a></p>
Realistic 3D avian vocal tract model demonstrates how shape affects sound filtering (Passer domesticus)
<p><span>Despite the complex geometry of songbird's vocal system, it was typically modelled as a tube or with simple mathematical parameters to investigate sound filtering. Here, we developed an adjustable computational acoustic model of a sparrow's upper vocal tract (<em>Passer domesticus</em>), derived from micro-CT scans. We discovered that a 20% tracheal shortening or a 20° beak gape increase caused the vocal tract harmonic resonance to shift towards higher pitch (11.7% or 8.8%, respectively), predominantly in the mid-range frequencies (3-6 kHz). The oropharyngeal-esophageal cavity (OEC), known for its role in sound filtering, was modelled as an adjustable 3D cylinder. For a constant OEC volume, an elongated cylinder induced a higher frequency shift than a wide cylinder (70% versus 37%). We found that the OEC volume adjustments can modify the OEC first harmonic resonance at low frequencies (1.5–3 kHz) and the OEC third harmonic resonance at higher frequencies (6-8 kHz). This work demonstrates the need to consider the realistic geometry of the vocal system to accurately quantify its effect on sound filtering and show that sparrows can tune the entire range of produced sound frequencies to their vocal system resonances, by controlling the vocal tract shape, especially through complex OEC volume adjustments.</span></p>
Supplementary material for "Sounding Obstacles for Social Distance Sonification".
<p>Supplementary material for the paper "Sounding Obstacles for social distance Sonification": Three videos (FmodI, FmodD, and NoFS) demonstrating three experimental conditions and 16 simulated videos (DD 01 - SS 06) presenting scenarios used in the experiment.</p>
Data from "Asymmetric visual capture of virtual sound sources in the distance dimension"
<p>This repository will contain raw and processed data used and described in:</p> <p><strong>Zahorik P (2022) Asymmetric visual capture of virtual sound sources in the distance dimension. Front. Neurosci. 16:958577. doi: 10.3389/fnins.2022.958577</strong></p>
Figure 1 in Acoustic analysis of vocalization and the behavioral response associated to sound production of the nine banded armadillo Dasypus novemcinctus (Mammalia, Cingulata, Dasypodidae)
Figure 1. Oscillogram (top) and spectrogram (bottom) of the agonistic vocalizations of Dasypus novemcinctus, HCLP-S 1028, recorded from individual M1. (A) A single vocalization composed of the pattern A-I, A-I, B-I, B-II; (B) A single vocalization composed of the pattern A-I, A-II, B-I, A-II, B-I, B-II.
Figure 2. M1 in Acoustic analysis of vocalization and the behavioral response associated to sound production of the nine banded armadillo Dasypus novemcinctus (Mammalia, Cingulata, Dasypodidae)
Figure 2. M1 (marked with white tape at the middle of its moveable bands) Behavior (A) when cornered after being submitted to the presence of other male subject, (B) at a second moment, when the other animal approaches from its back, and then (C) M1 bends its body left to prevent the contact, with the other male scratching the basis of its tail. In (D) the other male bipedally projects its belly against M1's back, and the latter finally changes its position.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.