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639 results for “Audio”
Object-based audio scene files for variations of the spatial arrangement in pop mixes for Wave Field Synthesis
<p>This entry contains object-based audio meta-data to generate the mixes published at http://dx.doi.org/10.5281/zenodo.61000.</p> <p>Have a look at README.md for further details.</p>
Dataset supplementing the article Einhäuser, W., Methfessel, P., & Bendixen, A. (2017). Newly acquired audio-visual associations bias perception in binocular rivalry. Vision Research, 133, 121-129.
<p>This dataset supplements the publication<br> Einhäuser, W., Methfessel, P., & Bendixen, A. (2017). Newly acquired audio-visual associations bias perception in binocular rivalry. Vision Research, 133, 121-129. doi: 10.1016/j.visres.2017.02.001</p> <p>Use is free for scientific purposes, provided the aforementioned reference is appropriately cited.<br> Description of files<br> - conditionsByObserver.csv<br> contains for each of the 16 observers the color and grating direction that had been coupled to either the low-pitch or the high-pitch tone<br> column 1: observer number<br> column 2: color associated with low-pitch tone<br> column 3: color associated with high-pitch tone<br> column 4: drift direction associated with low-pitch tone<br> column 5: drift direction associated with high-pitch tone</p> <p>- conditionsByObserver.mat contains the same information as matlab variables (as four vectors/cell arrays with one entry per observer)</p> <p>- toneByBlockAndTrial.csv<br> contains the conditions for all 18 rivalry trials (6 rivalry blocks with 3 trials each) for each observer<br> column 1: observer number<br> column 2: block number<br> column 3: trial number<br> column 4: tone (low [pitch], high [pitch], none) played in this trial<br> Note that due to a technical error for observer #16, block 6 was presented first, followed by 1,2,3,4,5; for all other observers blocks were used in the order given (1,2,3,4,5,6).</p> <p>- toneByBlockAndTrial.mat contains the same information as a 16x6x3 matrix named toneByBlockAndTrial ; tones are coded numerically (1-low pitch,2-high pitch,3-none)</p> <p>- eyeTraces.mat contains three cell arrays of dimensions 16x6x3 (observer x rivalry block x rivalry trial) called xEye, oknGain, and timeSinceTrialStart;</p> <p>o each entry of xEye contains the horizontal eye position for<br> the respective trial in eye-tracker coordinates (which correspond to screen pixels, except that (1/1) is the upper right rather than the upper left and values increase from right to left due to the setup configuration)</p> <p>o oknGain contains the gain computed from these eye positions.</p> <p>o timeSinceTrialStart contains the time in seconds since onset of the trial</p> <p><br> For all variables, the sampling rate is 500 Hz, in eye-tracker coordinates the speed of the grating is 240 units/ms. Blinks were removed from both eye-data variables, fast-phases were removed from the gain data. Removed data were set to NaN in eye-data variables.</p> <p>- Matlab functions figure1d.m, figure 2.m, figure3.m and figure4.m compute raw versions of the aforementioned paper's figures from the datafiles to exemplify their usage.</p> <p>[Note: In the originally published version of the article, the first two means and their standard errors of section 3.3 were stated incorrectly. All figures and statistical analyses are based on the correct data].</p>
Audio files referred to in Roettger, Timo B. (2017). Tonal Placement in Tashlhiyt - How an intonation system accommodates to adverse phonological environments.
<p>Audio files referred to in Roettger, Timo B. (2017). Tonal Placement in Tashlhiyt - How an intonation system accommodates to adverse phonological environments. Berlin: Language Science Press (DOI 10.5281/zenodo.814472)</p>
Audio Commons WP2 - Responses to user survey (Deliverable 2.1)
<p>These are the responses gathered from the user survey described in Deliverable 2.1 of the Audio Commons project.</p> <p>The survey contained 24 questions asking creatives working in music industry about various subjects like demographics, workflows they use and metadata they would like to use when searching for new audio content on the Web.</p> <p>The file formResponses.csv contains the actual responses, column names in the first row, fields separated by commas and enclosed in double quotes if needed (e.g., a comma was in the value).</p> <p>The Timestamp column contains the response timestamp, in DD/MM/YYYY hh:mm:ss format. </p> <p>Each other column is named as one of the survey questions and contain the corresponding responses.</p> <p>For the relevant context, please check the Audio Commons web page (http://www.audiocommons.org/) and specifically Deliverable 2.1.</p>
Audio clips of Orca (Orcinus orca) and non-orca sounds for the exploration of multiple acoustic representations
<p>Data and code associated with "Comparing acoustic representations for deep learning-based classification of underwater acoustic signals: a case study on orca (Orcinus orca) vocalizations."</p> <p>A collection of 9600 audio clips recorded by a hydrophone off San Juan Island, WA, USA. The clips are 3 seconds in duration with a sampling rate of 64KHz, and contain a variety of orca vocalizations (in the srkw folder), as well as non-orca sounds, both humpbacks (hb folder) and unspecified sounds typical of the location (neg folder). </p> <p>The code for each of the representations used in this study is also included.</p>
The Audio and Video Files of Handbook of Japanese Basic Verbs
<p>"KIHONDOUSHI HANDBOOK (Handbook of Japanese Basic Verbs) [基本動詞ハンドブッ ク] is a digital reference resource developed by the members of three collaborative research projects funded by the National Institute for Japanese Language and Linguistics (NINJAL), Tokyo, Japan during Oct. 2009 to March 2022. The reference work was developed with an aim to deepen the understanding of highly polysemous basic verbs in Japanese by way of providing detailed description of syntax and semantics of multiple meanings of the verb in question. To foster the ease of understanding, each meaning of a polysemous basic verb is illustrated with a couple of illustrative examples with audio and a few meanings, which are deemed to be difficult to understand, are illustrated with the help of audio-visual animations. The handbook consists of 190 headwords and includes 2,154 meanings, 12, 492 illustrative examples with audio and 521 audio-visual animations. On this site the audio and video files of the handbook is made available for researchers, teachers, and learners of Japanese language for the purpose of research on and education of Japanese polysemous basic verbs. Other data such as headword data will be available from the NINJAL repository site very soon.</p>
WikiMuTe: A web-sourced dataset of semantic descriptions for music audio
<p>This upload contains the supplementary material for our <a href="https://arxiv.org/abs/2312.09207" target="_blank" rel="noopener">paper</a> presented at the <a href="https://mmm2024.org/" target="_blank" rel="noopener">MMM2024 conference</a>.</p> <h2>Dataset</h2> <p>The dataset contains rich text descriptions for music audio files collected from Wikipedia articles.</p> <p>The audio files are freely accessible and available for download through the URLs provided in the dataset.</p> <h3>Example</h3> <p>A few hand-picked, simplified examples of the dataset. </p> <table> <tbody> <tr> <td> <p><strong>file</strong></p> </td> <td> <p><strong>aspects</strong></p> </td> <td> <p><strong>sentences</strong></p> </td> </tr> <tr> <td> <p><a href="https://upload.wikimedia.org/wikipedia/commons/7/7a/Bongo_sound.wav" target="_blank" rel="noopener"><strong>🔈 Bongo sound.wav</strong></a></p> </td> <td> <p>['bongoes', 'percussion instrument', 'cumbia', 'drums']</p> </td> <td> <p>['a loop of bongoes playing a cumbia beat at 99 bpm']</p> </td> </tr> <tr> <td> <p><a href="https://upload.wikimedia.org/wikipedia/commons/4/46/Example_of_double_tracking_in_a_pop-rock_song_%283_guitar_tracks%29.ogg" target="_blank" rel="noopener"><strong>🔈 Example of double tracking in a pop-rock song (3 guitar tracks).ogg</strong></a></p> </td> <td> <p>['bass', 'rock', 'guitar music', 'guitar', 'pop', 'drums']</p> </td> <td> <p>['a pop-rock song']</p> </td> </tr> <tr> <td> <p><a href="https://upload.wikimedia.org/wikipedia/commons/6/62/OriginalDixielandJassBand-JazzMeBlues.ogg" target="_blank" rel="noopener"><strong>🔈 OriginalDixielandJassBand-JazzMeBlues.ogg</strong></a></p> </td> <td> <p>['jazz standard', 'instrumental', 'jazz music', 'jazz']</p> </td> <td> <p>['Considered to be a jazz standard', 'is an jazz composition']</p> </td> </tr> <tr> <td> <p><a href="https://upload.wikimedia.org/wikipedia/commons/5/58/Colin_Ross_-_Etherea.ogg" target="_blank" rel="noopener"><strong>🔈 Colin Ross - Etherea.ogg</strong></a></p> </td> <td> <p>['chirping birds', 'ambient percussion', 'new-age', 'flute', 'recorder', 'single instrument', 'woodwind']</p> </td> <td> <p>['features a single instrument with delayed echo, as well as ambient percussion and chirping birds', 'a new-age composition for recorder']</p> </td> </tr> <tr> <td> <p><a href="https://upload.wikimedia.org/wikipedia/commons/8/8b/Belau_rekid_%28instrumental%29.oga" target="_blank" rel="noopener"><strong>🔈 Belau rekid (instrumental).oga</strong></a></p> </td> <td> <p>['instrumental', 'brass band']</p> </td> <td> <p>['an instrumental brass band performance']</p> </td> </tr> <tr> <td> <p><strong>...</strong></p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> </tr> </tbody> </table> <h3>Dataset structure</h3> <p>We provide three variants of the dataset in the <code>data</code> folder.</p> <p>All are described in the paper.</p> <ol> <li><code>all.csv</code> contains all the data we collected, without any filtering.</li> <li><code>filtered_sf.csv</code> contains the data obtained using the <em>self-filtering</em> method.</li> <li><code>filtered_mc.csv</code> contains the data obtained using the <em>MusicCaps</em> dataset method.</li> </ol> <h3>File structure</h3> <p>Each CSV file contains the following columns:</p> <ul> <li><code>file</code>: the name of the audio file</li> <li><code>pageid</code>: the ID of the Wikipedia article where the text was collected from</li> <li><code>aspects</code>: the short-form (tag) description texts collected from the Wikipedia articles</li> <li><code>sentences</code>: the long-form (caption) description texts collected from the Wikipedia articles</li> <li><code>audio_url</code>: the URL of the audio file</li> <li><code>url</code>: the URL of the Wikipedia article where the text was collected from</li> </ul> <h3>Citation</h3> <div> <p>If you use this dataset in your research, please cite the following paper:</p> <div> <pre><code>@inproceedings{wikimute,</code><br><code> title = {WikiMuTe: {A} Web-Sourced Dataset of Semantic Descriptions for Music Audio},</code><br><code> author = {Weck, Benno and Kirchhoff, Holger and Grosche, Peter and Serra, Xavier},</code><br><code> booktitle = "MultiMedia Modeling",</code><br><code> year = "2024",</code><br><code> publisher = "Springer Nature Switzerland",</code><br><code> address = "Cham",</code><br><code> pages = "42--56",</code><br><code> doi = {10.1007/978-3-031-56435-2_4},</code><br><code> url = {https://doi.org/10.1007/978-3-031-56435-2_4},</code><br><code>}</code></pre> </div> </div> <h3>License</h3> <p>The data is available under the <a href="https://creativecommons.org/licenses/by-sa/3.0/" target="_blank" rel="noopener">Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0) license</a>.</p> <p>Each entry in the dataset contains a URL linking to the article, where the text data was collected from.</p>
WaveFake: A data set to facilitate audio DeepFake detection
<p>The main purpose of this data set is to facilitate research into audio DeepFakes. We hope that this work helps in finding new detection methods to prevent such attempts. These generated media files have been increasingly used to commit <a href="https://www.vice.com/en/article/pkyqvb/deepfake-audio-impersonating-ceo-fraud-attempt">impersonation attempts</a> or <a href="https://www.wired.com/story/telegram-still-hasnt-removed-an-ai-bot-thats-abusing-women/">online harassment</a>. You can find the accompanying code repository on <a href="https://github.com/RUB-SysSec/WaveFake">GitHub</a>.</p> <p>The data set consists of 104,885 generated audio clips (16-bit PCM wav). We examine multiple networks trained on two reference data sets. First, the <a href="https://keithito.com/LJ-Speech-Dataset/">LJSpeech</a> data set consisting of 13,100 short audio clips (on average 6 seconds each; roughly 24 hours total) read by a female speaker. It features passages from 7 non-fiction books and the audio was recorded on a MacBook Pro microphone. Second, we include samples based on the <a href="https://sites.google.com/site/shinnosuketakamichi/publication/jsut">JSUT</a> data set, specifically, basic5000 corpus. This corpus consists of 5,000 sentences covering all basic kanji of the Japanese language (4.8 seconds on average; roughly 6.7 hours total). The recordings were performed by a female native Japanese speaker in an anechoic room. Finally, we include samples from a full text-to-speech pipeline (16,283 phrases; 3.8s on average; roughly 17.5 hours total). Thus, our data set consists of approximately 175 hours of generated audio files in total. Note that we do not redistribute the reference data.</p> <p>We included a range of architectures in our data set:</p> <ul> <li><a href="https://arxiv.org/abs/1910.06711">MelGAN</a></li> <li><a href="https://arxiv.org/abs/1910.11480">Parallel WaveGAN</a></li> <li><a href="https://arxiv.org/abs/2005.05106">Multi-Band MelGAN</a></li> <li><a href="http://arxiv.org/abs/2005.05106">Full-Band MelGAN</a></li> <li><a href="https://arxiv.org/abs/2010.05646">HiFi-GAN</a></li> <li><a href="https://arxiv.org/abs/1811.00002">WaveGlow</a></li> </ul> <p>Additionally, we examined a bigger version of MelGAN and include samples from a full TTS-pipeline consisting of a conformer and parallel WaveGAN model.</p> <p><strong>Collection Process</strong></p> <p>For WaveGlow, we utilize the <a href="https://github.com/NVIDIA/waveglow">official implementation</a> (commit 8afb643) in conjunction with the official pre-trained network on <a href="https://pytorch.org/hub/nvidia_deeplearningexamples_waveglow/">PyTorch Hub</a>. We use a popular implementation available on <a href="https://github.com/kan-bayashi/ParallelWaveGAN">GitHub</a> (commit 12c677e) for the remaining networks. The repository also offers pre-trained models. We used the pre-trained networks to generate samples that are similar to their respective training distributions, <a href="https://keithito.com/LJ-Speech-Dataset/">LJ Speech</a> and <a href="https://sites.google.com/site/shinnosuketakamichi/publication/jsut">JSUT</a>. When sampling the data set, we first extract Mel spectrograms from the original audio files, using the pre-processing scripts of the corresponding repositories. We then feed these Mel spectrograms to the respective models to obtain the data set. For sampling the full TTS results, we use the <a href="https://github.com/espnet/espnet">ESPnet</a> project. To make sure the generated phrases do not overlap with the training set, we downloaded the <a href="https://commonvoice.mozilla.org/en/datasets">common voices data set</a> and extracted 16.285 phrases from it.</p> <p>This data set is licensed with a CC-BY-SA 4.0 license.</p> <p>This work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy -- EXC-2092 CaSa -- 390781972.</p>
Audio recordings of COVID-19 positive individuals from the prospective Predi-COVID cohort study with their ageusia and anosmia status
<p>We uploaded 1636 audio recordings originating from 259 distinct participants in the prospective Predi-COVID cohort study recruited between May 2020 and May 2021. The audios have been converted from their original format into WAV files. The audio name structure integrates the participant ID, the recording date and time of the audio recording, the type of audio (Type 1: reading of a text, Type2: hold the [a] vowel), the original audio format, and the symptomatic status for ageusia and anosmia (1: symptomatic, 0: asymptomatic) as such:</p> <p>predi-covid_{participant}{recording date and time}{type of audio}{original format}{sympyomatic status}.wav</p>
Video Examples from: Creating Audio Object-focused Acoustic Environments for Room-Scale Virtual Reality
<p>Video recordings illustrating the issues and possible solutions mentioned in the paper.</p> <p>Please use headphones when watching the videos.</p>
ORPHEUS The Movie (MPEG-H audio track)
<p>This is an experimental version of the mid length version of the final ORPHEUS project movie<br> (as published here <a href="https://youtu.be/AW2EB7-zxf4">ORPHEUS-Audio on Youtubel</a>) <br> It has been authored and encoded using MPEG-H audio, thus including these NGA features:</p> <ul> <li>6 presets (perspectives): Main - Underground Narrator - Narrator App EN - Narrator App De - Underground App EN - Underground App DE</li> <li>the narrator level can be adjusted in all presets</li> </ul> <p>Mind: Successful audio playback requires a MPEG-H compatible device!</p>
Test Stimuli for Context Based Evaluation of the OPUS Audio Codec
<p>A dataset that includes the 360° video .mp4 files and Ambisonic audio .wav files used in the listening tests for "Context Based Evaluation of the OPUS Audio Codec for Spatial Audio Content in Virtual Reality".</p>
FOAMS: Original Audio Files
<p>The original audio files used in the Free Open-Access Misophonia Stimuli (FOAMS) project to curate a freely available database of sound stimuli intended for misophonia research.</p>
Effects of audio-motor training on spatial representations in long-term late blindness
<p>Datasets for behavioural data:</p> <p>-<em>Auditory horizontal localization task</em></p> <p>- <em>Auditory vertical localization task</em></p> <p>- <em>Position matching task</em></p> <p>- <em>Proprioceptive midline task</em></p> <p>Dataset for EEG data:</p> <p>- Spatial bisection task: mean ERP amplitude for 50-90ms timew window for each trial, separately for condition, session and roi</p> <p> </p>
Proteinoid-Polyaniline Neuromorphic Composites for Audio Recognition
<p>The data presented is in CSV format and includes potentials vs. time and the magnitude of the signal in dB vs. time. This data is related to the research titled "Proteinoid-Polyaniline Neuromorphic Composites for Audio Recognition."</p>
The UK COVID-19 Vocal Audio Dataset
<p>The UK COVID-19 Vocal Audio Dataset is designed for the training and evaluation of machine learning models that classify SARS-CoV-2 infection status or associated respiratory symptoms using vocal audio. The UK Health Security Agency recruited voluntary participants through the national Test and Trace programme and the REACT-1 survey in England from March 2021 to March 2022, during dominant transmission of the Alpha and Delta SARS-CoV-2 variants and some Omicron variant sublineages. Audio recordings of volitional coughs, exhalations, and speech (speech not available in open access version) were collected in the 'Speak up to help beat coronavirus' digital survey alongside demographic, self-reported symptom and respiratory condition data, and linked to SARS-CoV-2 test results. The UK COVID-19 Vocal Audio Dataset represents the largest collection of SARS-CoV-2 PCR-referenced audio recordings to date. PCR results were linked to 70,794 of 72,999 participants and 24,155 of 25,776 positive cases. Respiratory symptoms were reported by 45.62% of participants. This dataset has additional potential uses for bioacoustics research, with 11.30% participants reporting asthma, and 27.20% with linked influenza PCR test results.</p> <h3>Contents</h3> <ul> <li><strong>participant_metadata.csv</strong> row-wise, participant identifier indexed information on participant demographics and health status. Please see <a href="https://arxiv.org/pdf/2212.07738.pdf">A large-scale and PCR-referenced vocal audio dataset for COVID-19</a> for a full description of the dataset.</li> <li><strong>audio_metadata.csv</strong> row-wise, participant identifier indexed information on three recorded audio modalities, including audio filepaths. Please see <a href="https://arxiv.org/pdf/2212.07738.pdf">A large-scale and PCR-referenced vocal audio dataset for COVID-19</a> for a full description of the dataset.</li> <li><strong>train_test_splits.csv</strong> row-wise, participant identifier indexed information on train test splits for the following sets: 'Randomised' train and test set, Standard' train and test set, Matched' train and test sets, 'Longitudinal' test set and 'Matched Longitudinal' test set. Please see <a href="https://arxiv.org/abs/2212.08570">Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers</a> for a full description of the train test splits.</li> <li><strong>audio/ </strong>directory containing all the recordings in .wav format <ul> <li>Due to the large size of the dataset, to assist with ease of download, the audio files have been zipped into <strong>covid_data.z{ip, 01-24}.</strong> This enables the dataset to be downloaded in short periods, reducing the chances of a dropped internet connection scuppering progress. To unzip, first, ensure that all zip files are in the same directory. Then run the command 'unzip covid_data.zip' or right-click on 'covid_data.zip' and use a programme such as 'The Unarchiver' to open the file.</li> <li>Once extracted, to check the validity of the download, please run the 'python Turing-RSS-Health-Data-Lab-Biomedical-Acoustic-Markers/data-paper/unit-tests.py. All tests should pass with no exceptions. Please clone the GitHub repo detailed below.</li> </ul> </li> <li><strong>README.md</strong> full dataset descriptor.</li> <li><strong>DataDictionary_UKCOVID19VocalAudioDataset_OpenAccess.xlsx </strong>descriptor of each dataset attribute with the percentage coverage.</li> </ul> <h3>Code Base</h3> <p>The accompanying code can be found here: https://github.com/alan-turing-institute/Turing-RSS-Health-Data-Lab-Biomedical-Acoustic-Markers</p> <h3>Citations:</h3> <p>Please cite.</p> <p>@article{coppock2024audio,</p> <p> author = {Coppock, Harry and Nicholson, George and Kiskin, Ivan and Koutra, Vasiliki and Baker, Kieran and Budd, Jobie and Payne, Richard and Karoune, Emma and Hurley, David and Titcomb, Alexander and Egglestone, Sabrina and Cañadas, Ana Tendero and Butler, Lorraine and Jersakova, Radka and Mellor, Jonathon and Patel, Selina and Thornley, Tracey and Diggle, Peter and Richardson, Sylvia and Packham, Josef and Schuller, Björn W. and Pigoli, Davide and Gilmour, Steven and Roberts, Stephen and Holmes, Chris},</p> <p> title = {Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers},</p> <p> journal = {Nature Machine Intelligence},</p> <p> year = {2024},</p> <p> doi = {https://doi.org/10.1038/s42256-023-00773-8}</p> <p>}</p> <p>@article{budd2024,</p> <p> author={Jobie Budd and Kieran Baker and Emma Karoune and Harry Coppock and Selina Patel and Ana Tendero Cañadas and Alexander Titcomb and Richard Payne and David Hurley and Sabrina Egglestone and Lorraine Butler and George Nicholson and Ivan Kiskin and Vasiliki Koutra and Radka Jersakova and Peter Diggle and Sylvia Richardson and Bjoern Schuller and Steven Gilmour and Davide Pigoli and Stephen Roberts and Josef Packham Tracey Thornley Chris Holmes},</p> <p> title={A large-scale and PCR-referenced vocal audio dataset for COVID-19},</p> <p> journal={Scientific Data},</p> <p> year={2024},</p> <p> doi = {https://doi.org/10.1038/s41597-024-03492-w}</p> <p>}</p> <p>@article{Pigoli2022,</p> <p> author={Davide Pigoli and Kieran Baker and Jobie Budd and Lorraine Butler and Harry Coppock and Sabrina Egglestone and Steven G.\ Gilmour and Chris Holmes and David Hurley and Radka Jersakova and Ivan Kiskin and Vasiliki Koutra and George Nicholson and Joe Packham and Selina Patel and Richard Payne and Stephen J.\ Roberts and Bj\"{o}rn W.\ Schuller and Ana Tendero-Ca$\tilde{n}$adas and Tracey Thornley and Alexander Titcomb},</p> <p>title={Statistical Design and Analysis for Robust Machine Learning: A Case Study from Covid-19},</p> <p> year={2022},</p> <p> journal={arXiv},</p> <p> doi = {10.48550/ARXIV.2212.08571}</p> <p>}</p> <p> </p> <h3>The Dublin Core™ Metadata Initiative</h3> <p> </p> <p>- Title: The UK COVID-19 Vocal Audio Dataset, Open Access Edition.</p> <p>- Creator: The UK Health Security Agency (UKHSA) in collaboration with The Turing-RSS Health Data Lab.</p> <p>- Subject: COVID-19, Respiratory symptom, Other audio, Cough, Asthma, Influenza.</p> <p>- Description: The UK COVID-19 Vocal Audio Dataset Open Access Edition is designed for the training and evaluation of machine learning models that classify SARS-CoV-2 infection status or associated respiratory symptoms using vocal audio. The UK Health Security Agency recruited voluntary participants through the national Test and Trace programme and the REACT-1 survey in England from March 2021 to March 2022, during dominant transmission of the Alpha and Delta SARS-CoV-2 variants and some Omicron variant sublineages. Audio recordings of volitional coughs and exhalations were collected in the 'Speak up to help beat coronavirus' digital survey alongside demographic, self-reported symptom and respiratory condition data, and linked to SARS-CoV-2 test results. The UK COVID-19 Vocal Audio Dataset Open Access Edition represents the largest collection of SARS-CoV-2 PCR-referenced audio recordings to date. PCR results were linked to 70,794 of 72,999 participants and 24,155 of 25,776 positive cases. Respiratory symptoms were reported by 45.62% of participants. This dataset has additional potential uses for bioacoustics research, with 11.30% participants reporting asthma, and 27.20% with linked influenza PCR test results.</p> <p>- Publisher: The UK Health Security Agency (UKHSA).</p> <p>- Contributor: The UK Health Security Agency (UKHSA) and The Alan Turing Institute.</p> <p>- Date: 2021-03/2022-03</p> <p>- Type: Dataset</p> <p>- Format: Waveform Audio File Format audio/wave, Comma-separated values text/csv</p> <p>- Identifier: <strong>10.5281/zenodo.10043977</strong></p> <p>- Source: The UK COVID-19 Vocal Audio Dataset Protected Edition, accessed via application to <a href="https://www.gov.uk/government/publications/accessing-ukhsa-protected-data/accessing-ukhsa-protected-data">Accessing UKHSA protected data</a>.</p> <p>- Language: eng</p> <p>- Relation: The UK COVID-19 Vocal Audio Dataset Protected Edition, accessed via application to <a href="https://www.gov.uk/government/publications/accessing-ukhsa-protected-data/accessing-ukhsa-protected-data">Accessing UKHSA protected data</a>.</p> <p>- Coverage: United Kingdom, 2021-03/2022-03.</p> <p>- Rights: Open Government Licence version 3 (OGL v.3), © Crown Copyright UKHSA 2023.</p> <p>- accessRights: When you use this information under the Open Government Licence, you should include the following attribution: The UK COVID-19 Vocal Audio Dataset Open Access Edition, UK Health Security Agency, 2023, licensed under the <a href="https://www.nationalarchives.gov.uk/doc/open-government-licence/">Open Government Licence v3.0</a> and cite the papers detailed above.</p> <p> </p>
Audio proceedings from CONFLICT: Collections, Archives and Heritage symposium
<p>The <em>CONFLICT: Archives, Collections and Heritage</em> workshop (in-person and online) explores the topic of cultural heritage, archives and museum collections in conflict and post-conflict times. Starting with Cambodia’s art history and archaeology, discussions will expand to a global perspective. It is organised by Magali An Berthon from the Center for Textile Research at the University of Copenhagen on 25 March 2024, in conjunction with the exhibition “The Art of Ikat: A Cambodian Renaissance” which opens on 22 February 2024, at the Royal University Library, University of Copenhagen South Campus. Linda Sok and Sophea Oum, two of the artists featured in the exhibition, will be in attendance.</p> <p>How does conflict affect the recovery, preservation and conservation of museum collections and archives? Does it also affect the meaning and significance of such collections, whether they survived or were lost? Facing disappearance and destruction how do artists and artisans offer invaluable responses toward restoration and healing? These are some of the key questions animating this day of discussion centred on innovative research approaches.</p> <p>This event was supported by the Carlsberg Foundation and the David Fond og Samling.</p>
Duhumbi Agricultural Practices - Description, Audio, Video, Photos
<p>This collection of videos, audio and photo files displays Duhumbi agricultural practices as they were conducted between 2012 and 2017. Accompanying video and picture files illustrating the text files can be found at the end of this document.</p> <p>Traditionally, the people of the Chug valley depended on a mix of agriculture and animal husbandry for their livelihoods, supplemented by hunting and collection of forest produce. The Chug valley and the Sangthi valley are the only places in West Kameng district where relatively large-scale wetland rice cultivation takes place. This rice has for long been the main item in the barter trade, as well as the main item collected as tax by the erstwhile Tibetan administration and raided by the Miji.</p> <p>Agriculture has always been the main-stay of the local economy, not just in terms of self-sufficiency, but also in terms of barter trade. The agricultural produce, mainly the rice, was bartered for other food and other items, a practice that, despite increasing monetisation of the economic system, continues till date.</p> <p>This material is made freely available to everyone for informative or scientific purposes as long as the source (this DOI) / the collectors are properly credited. Please note that use of the material for commercial purposes <em><strong>of any kind</strong>, which includes conversion into commercial audio-visual media (documentaries etc.), storage and dissemination through sites that require registration & payment for access, or sites that rely on advertisement (including YouTube) </em>is <strong>not</strong> permitted without <strong>specific written consent</strong> from the speakers and their community, obtained through the collectors of the material. By downloading our material, you agree to these restrictions.</p> <p>This data set falls under the Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license. This license lets you remix, tweak, and build upon this work non-commercially, as long as you credit us and license your new creations under the identical terms. License Deed on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>. Legal Code on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode</a>.</p> <p>Tim Bodt: bodttim (at) gmail (dot) com</p>
EEG and audio dataset for auditory attention decoding
<p>This dataset contains EEG recordings from 18 subjects listening to one of two competing speech audio streams. Continuous speech in trials of ~50 sec. was presented to normal hearing listeners in simulated rooms with different degrees of reverberation. Subjects were asked to attend one of two spatially separated speakers (one male, one female) and ignore the other. Repeated trials with presentation of a single talker were also recorded. The data were recorded in a double-walled soundproof booth at the Technical University of Denmark (DTU) using a 64-channel Biosemi system and digitized at a sampling rate of 512 Hz. Full details can be found in:</p> <ul> <li><strong>Søren A. Fuglsang, Torsten Dau & Jens Hjortkjær (2017): Noise-robust cortical tracking of attended speech in real-life environments. <em>NeuroImage</em>, 156, 435-444</strong></li> </ul> <p>and</p> <ul> <li><strong>Daniel D.E. Wong, Søren A. Fuglsang, Jens Hjortkjær, Enea Ceolini, Malcolm Slaney & Alain de Cheveigné: A Comparison of Temporal Response Function Estimation Methods for Auditory Attention Decoding. Frontiers in Neuroscience, </strong><a href="https://doi.org/10.3389/fnins.2018.00531">https://doi.org/10.3389/fnins.2018.00531</a></li> </ul> <p>The data is organized in format of the publicly available <a href="https://zenodo.org/record/1198430">COCOHA Matlab Toolbox</a>. The preproc_script.m demonstrates how to import and align the EEG and audio data. The script also demonstrates some EEG preprocessing steps as used the Wong et al. paper above. The AUDIO.zip contains wav-files with the speech audio used in the experiment. The EEG.zip contains MAT-files with the EEG/EOG data for each subject. The EEG/EOG data are found in <strong>data.eeg</strong> with the following channels:</p> <ul> <li>channels 1-64: scalp EEG electrodes</li> <li>channel 65: right mastoid electrode</li> <li>channel 66: left mastoid electrode</li> <li>channel 67: vertical EOG below right eye</li> <li>channel 68: horizontal EOG right eye</li> <li>channel 69: vertical EOG above right eye</li> <li>channel 70: vertical EOG below left eye</li> <li>channel 71: horizontal EOG left eye</li> <li>channel 72: vertical EOG above left eye</li> </ul> <p>The <strong>expinfo</strong> table contains information about experimental conditions, including what what speaker the listener was attending to in different trials. The expinfo table contains the following information:</p> <ul> <li>attend_mf: attended speaker (1=male, 2=female)</li> <li>attend_lr: spatial position of the attended speaker (1=left, 2=right)</li> <li>acoustic_condition: type of acoustic room (1= anechoic, 2= mild reverberation, 3= high reverberation, see Fuglsang et al. for details)</li> <li>n_speakers: number of speakers presented (1 or 2)</li> <li>wavfile_male: name of presented audio wav-file for the male speaker</li> <li>wavfile_female: name of presented audio wav-file for the female speaker (if any)</li> <li>trigger: trigger event value for each trial also found in data.event.eeg.value</li> </ul> <p>DATA_preproc.zip contains the preprocessed EEG and audio data as output from preproc_script.m.</p> <p>The dataset was created within the <a href="https://cocoha.org/">COCOHA</a><a href="https://cocoha.org/"> Project</a>: Cognitive Control of a Hearing Aid</p>
Kusunda 250 Word List Audio Files
<p>This data set contains 2 zip files and 3 pdf files containing the cut sound files of the elicited 250-concept word list (plus numerous additional concepts and lexical items) recorded with the last two Kusunda speakers from Nepal, Gyani Maiya Sen Kusunda and Kamala Khatri (Sen Kusunda), on in late July and early August 2019, in Kathmandu, Nepal.</p> <p>As per our knowledge, these are the first recordings of Kusunda that are available in the public domain.</p> <p>The zip files, when unpacked, will reveal several hundreds of cut sound files. At least the concepts from the list are triple repeated. There are also numerous additional concepts on recording. The pdf files contain explanations regarding the elicited concepts as well as the way in which the forms of the two speakers were used to come to an underlying form, a 'reconstruction' of some sorts. </p> <p>The file numbers of the individual sound files refer to both the speakers (GM, K), to the date of recording and the exact origin file number. More information regarding a certain concept or pronunciation can be found in these respective origin sound files.</p> <p>Please note that the actual transcriptions in IPA may differ between the file names of the individual sound recordings, the individual speaker's pdf file, and the aggregated pdf file. We request users of these data to take the transcriptions in the aggregated pdf file as the most recent ones, or alternatively contact us for the most recent, updated transcriptions.</p> <p>This research was funded by a 2,000 USD grant from the Endangered Language Fund (<a href="http://www.endangeredlanguagefund.org/">http://www.endangeredlanguagefund.org/</a>), a 700 euro contribution by the European Research Council Starting Grant 715618 “Computer-Assisted Language Comparison” (<a href="http://calc.digling.org/">http://calc.digling.org</a>), and a total of 2,320 euros raised through crowdfunding at GoFundMe (<a href="https://www.gofundme.com/f/saving-the-kusunda-language-in-nepal">https://www.gofundme.com/f/saving-the-kusunda-language-in-nepal</a>). Many thanks to all generous contributors.</p> <p>This material is made freely available to everyone for informative or scientific purposes as long as the source (this DOI) / the collectors are properly credited. Please note that use of the material for commercial purposes <strong><em>of any kind</em></strong><em>, which includes conversion into commercial audio-visual media (documentaries etc.), (paid) public screening other than for educational purposes, storage and dissemination through sites that require registration & payment for access, or sites that rely on advertisement (including YouTube) </em>is <strong>not</strong> permitted without <strong>specific written consent</strong> from the speakers and their community, obtained through the collectors of the material. By downloading our material, you agree to these restrictions.</p> <p>This data set falls under the Create Commons Attribution 4.0 International / Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license. This license lets you remix, tweak, and build upon this work non-commercially, as long as you credit us and license your new creations under the identical terms. License Deed on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>. Legal Code on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode</a>.</p> <p>We greatly value feedback, suggestions, advice, analysis etc. based on this material which will help in the description of the Kusunda language, especially any comments and suggestions that will enable the revitalisation of the language, including a standardisation of the phonology and a phonologically consistent but also practical orthography in both देवनागरी Devanāgarī and Roman script.</p> <p>Uday Raj Aaley: aarambhkhabar (at) gmail (dot) com</p> <p>Tim Bodt: bodttim (at) gmail (dot) com</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.