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503 results for “Voice”
Data from: More than just two sexes: the neural correlates of voice gender perception in gender dysphoria
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Voices from the field cultural capital transformation issues for dance practitioners
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Data from: Domestic dogs and puppies can use human voice direction referentially
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Data from: Evaluation of the severity of major depression using a voice index for emotional arousal
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Data from: Linking men‘s voice pitch to actual and perceived trustworthiness across domains
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Data from: Lower vocal tract morphologic adjustments are relevant for voice timbre in singing
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VOICe Dataset
<p>VOICe: A novel dataset for the development and evaluation of generalizable sound event detection domain adaptation methods!<br> <br> VOICe consists of 1449 different mixtures of three different sound events ("baby crying", "glass breaking", and "gunshot"):</p> <ul> <li>1242 mixtures with background noise of three different categories of acoustic scenes ("vehicle"," outdoors", and "indoors"), mixed under 2 SNR values (-3, -9 dB), that is 207 mixtures x 3 acoustic scenes x 2 SNRs = 1242</li> <li> 207 mixtures without any background noise.</li> </ul> <p> VOICe is offered for sound event detection domain adaptation from one acoustic scene to another, or between sound events with background noise and without background noise.<br> <br> You can also find more information about the dataset in our paper: <a href="https://www.google.com/url?q=https://arxiv.org/pdf/1911.07098.pdf&sa=D&source=hangouts&ust=1578483524111000&usg=AFQjCNFfL1pv0_wXhnpNPEktej3tK_eqIw">https://arxiv.org/pdf/1911.07098.pdf</a></p>
MATERIALS: Impact of face masks on voice radiation
<p>Speaker directivities of a HEAD acoustics HMS II.3 head and mouth simulator wearing different mouth and nose covers in the SOFA format, as "GeneralFIR" convention, as described in [1].</p> <p>[1] Poerschmann C., Luebeck T., & J.M. Arend (2020). "Impact of face masks on voice radiation". The Journal of the Acoustical Society of America 148(6) pp.3663-3670 (2020)</p> <p>________________________________________________________________________________________________________</p> <p><strong>Name convention:</strong></p> <p>Directivity_no_mask_ref: Reference measurement: HMS without a mask</p> <p>Directivity_mask1-6: all measured masks, according to the numeration below. All masks were measured two times indicated with "measurement2".</p> <p><strong>Medical masks:</strong><br> 1) Disposable Triple Layer Filter Dustproof Earloop Non Woven medical mask, (brandname: Arvin, Protection Class DS3)</p> <p><strong>Filtering facepiece respirators:</strong><br> 2) Three-dimensional respirator, Protection Class KN 95 (manufacturer: Suzhou Jinruida Protective Equipment Co, Inc.)<br> 3) MB 21 - fine dust respirator, Protection Class FFP 2 (manufacturer: MB Filter Products AB, Sävedalen, Sweden)</p> <p><strong>Cloth face coverings:</strong><br> 4) Microfibre scarf (manufacturer: Rose, material: 100\% polyester)<br> 5) Cloth Face Covering (manufacturer: Modeatelier Scharn, Engelskirchen, Germany)<br> 6) Hand-made cloth face covering: Double Layers cotton (manufacturer Stoffliebe, Gelsenkirchen, Germany)</p> <p>________________________________________________________________________________________________________</p> <p><strong>Contact:</strong><br> Christoph Pörschmann, Tim Lübeck, and Johannes M. Arend<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</p> <p><a href="http://www.th-koeln.de/personen/christoph.poerschmann/">https://www.th-koeln.de/personen/christoph.poerschmann/</a><br> <a href="https://www.th-koeln.de/personen/tim.luebeck/">https://www.th-koeln.de/personen/tim.luebeck/</a><br> <a href="https://www.th-koeln.de/personen/johannes.arend/">https://www.th-koeln.de/personen/johannes.arend/</a></p>
Data from: Auditory traits of "own voice"
People perceive their recorded voice differently from their actively spoken voice. The uncanny valley theory proposes that as an object approaches humanlike characteristics, there is an increase in the sense of familiarity; however, eventually a point is reached where the object becomes strangely similar and makes us feel uneasy. The feeling of discomfort experienced when people hear their recorded voice may correspond to the floor of the proposed uncanny valley. To overcome the feeling of eeriness of own-voice recordings, previous studies have suggested equalization of the recorded voice with various types of filters, such as step, bandpass, and low-pass, yet the effectiveness of these filters has not been evaluated. To address this, the aim of experiment 1 was to identify what type of voice recording was the most representative of one's own voice. The voice recordings were presented in five different conditions: unadjusted recorded voice, step filtered voice, bandpass filtered voice, low-pass filtered voice, and a voice for which the participants freely adjusted the parameters. We found large individual differences in the most representative own-voice filter. In order to consider roles of sense of agency, experiment 2 investigated if lip-synching would influence the rating of own voice. The result suggested lip-synching did not affect own voice ratings. In experiment 3, based on the assumption that the voices used in previous experiments corresponded to continuous representations of non-own voice to own voice, the existence of an uncanny valley was examined. Familiarity, eeriness, and the sense of own voice were rated. The result did not support the existence of an uncanny valley. Taken together, the experiments led us to the following conclusions: there is no general filter that can represent own voice for everyone, sense of agency has no effect on own voice rating, and the uncanny valley does not exist for own voice, specifically.
The Split of Proto-Tai Voiced Stop Consonants in Nung of Trang Dinh District
<p>In most modern Tai varieties, Proto-Tai voiced stops are devoiced, and one Proto-Tai voiced stop is realized as one consonant. For example, Proto-Tai *b- is realized as p- or ph-. In Trang Dinh Nung, a variety of the Nung language in Trang Dinh district, Lang Son province, Vietnam, however, Proto-Tai voiced stops split into two consonants. For example, *b- splits into p- and ph-. This presentation is aimed at showing that this phenomenon in Trang Dinh Nung is related to tone.</p>
Supplementary material for "Effects of hand postures on voice directivity"
<p>The repository contains measured voice directivities while performing static hand postures. The directivities were measured in the anechoic chamber of TH Köln applying a surrounding spherical microphone array, which has a diameter of<br> 2 m and a shape of a pentakis dodecahedron. The datasets were stored in the SOFA-format and include two measurements for each of the 13 subjects.</p> <p>Each subject's dataset contains directivities of the following conditions</p> <ul> <li>REF: Reference measurement - normal articulation of an [a].</li> <li>HFM: Holding a hand in front of the mouth while articulating an [a].</li> <li>CAM: Cupping the hand around the mouth while articulating an [a].</li> </ul> <p> </p> <p>The .pdf file contains</p> <ul> <li>general information on the voice directivity files in the SOFA format</li> <li>information on the indices and names of the SOFA-files</li> <li>Polar plots of the directivity patterns in third-octave bands </li> </ul> <p> </p> <p>The .zip files contain</p> <ul> <li>voice directivity patterns in the SOFA format sampled on the sparse measuring grid</li> <li>voice directivity patterns in the SOFA format upsampled to a dense grid</li> </ul> <p> </p> <p>The Matlab script provides</p> <ul> <li>an example reading a dataset, performing spatial upsampling if required, and creating some basic plots. </li> </ul>
Spirit Voice Reciever Box
Spirit Voice Reciever Box model like in game At Dead Of Night. Made without sizes, so it may differ from the model in original Game. Source: Objaverse 1.0 / Sketchfab
Figure 1 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 1 - Amazon's Mechanical Turk Web site.
Figure 8 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 8 - Example artifacts in mPower voice recordings.
Data for: Evaluating motivation attributes and interaction elements in user adoption of voice intelligent assistant
<p class="MsoNormal"><span>Artificial intelligence (AI) has come to human life in various forms. As external activities decrease after COVID-19, people treat voice intelligent assistant (VIA) as a friend or secretary. This study suggests a conceptual model to identify the contributors to the continuance intention of VIA users. Data is collected from 262 users who use VIAs in their daily life for validating the model. The current research conducted partial least square structural equation modeling to validate the proposed model. The findings indicated that cabin fever syndrome significantly affects both utilitarian motivation and hedonic motivation. Loneliness in COVID-19 causes utilitarian motivation. The results verified that utilitarian motivation is the determinant of perceived usefulness, interaction, and voice attractiveness. The findings of the study figured out that hedonic motivation has a significant influence on interaction, parasocial interaction, and voice attractiveness. The analysis uncovered that interaction impacts satisfaction and continuance intention. The findings validated that voice attractiveness is significantly associated with satisfaction. Satisfaction is the determinant of continuance intention.</span></p>
Spoken NER Dataset based on Common Voice 11.0
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Voice Feminisation in Transgender Women
ClinicalTrials.gov study NCT03899896. IPD Sharing: NO. Countries: 1. Publications: 0.
The Voice as a Tool to Detect Recurrence of Laryngeal and Hypopharyngeal Cancer
ClinicalTrials.gov study NCT05894070. IPD Sharing: NO. Countries: 1. Publications: 0.
Effects of Voice Records on Patient Outcomes Surgical Intensive Care Unit
ClinicalTrials.gov study NCT07007754. IPD Sharing: YES. Countries: 1. Publications: 0.
Making HD Voices Heard
ClinicalTrials.gov study NCT05268029. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.