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Dataset results
16 results for “audio processing”
FOAMS: Processed Audio Files
<p>The processed audio files included in the Free Open-Access Misophonia Stimuli (FOAMS) project to curate a freely available database of sound stimuli intended for misophonia research.</p> <p>If you use this database, please credit it as follows:</p> <p>Orloff, D. M., Benesch, D., & Hansen, H. A. (2023). Curation of FOAMS: a Free Open-Access Misophonia Stimuli Database. <em>Journal of Open Psychology Data</em>, <em>11</em>(1).</p>
TimeSide API as an audio processing web service
<p>Audio descriptors can help to analyze, classify and compare sounds by their own characteristics. For large datasets,it is often needed to store the results of the analyses in order to keep everything sustainable and comparable, especially in machine learning usecases. This demo will show how to use the TimeSide REST API as a remote service to process descriptors in order to embed the results in any web application. The new version of the TimeSide player will be presented as a general example of using the dedicated javascript SDK to produce new kinds of applications involving visualization and collaborative annotation.</p>
Yongning Na for Natural Language Processing: a single-speaker audio corpus with transcriptions
<p><em>(français ci-dessous)</em></p> <p>This archive contains a dataset (audio files and transcriptions) of a minority language, Yongning Na (Glottocode: yong1288; closest iso 639-3 code: nru). The archive contains a subset of the Na corpus of the Pangloss Collection: it is a single-speaker corpus, consisting of all the audio resources transcribed, for the main speaker of this corpus (Ms. LATAMI Dashilame).<br> The corpus is versioned, so that the experiments carried out on these resources (for linguistic research or for Natural Language Processing) are fully reproducible. All relevant information is contained in YAML files (.yml extension; one in French, one in English).<br> The data sub-folder contains the converted and demultiplexed audio files, as well as the annotations associated with each channel of the audio files.<br> The summary files contain, among other things, the list of graphemes used in the language (complex graphemes are particularly important), as well as information on the various resources (audio and annotations), such as their identifiers (DOIs) and links to the original files.<br> From a computational point of view, the list of DOIs of the audios and annotations described in this YAML file is sufficient to generate this corpus at a given time. A corpus like the present one can be viewed as the version, at a given time, of a set of documents in the Pangloss collection: a corpus as it stands at a precise version.</p> <p>Further information is available from <a href="https://gitlab.com/lacito/outilspangloss">https://gitlab.com/lacito/outilspangloss</a></p> <p>---------------</p> <p>Cette archive contient un jeu de données (audios et transcriptions) d’une langue à tradition orale, le na de Yongning (Glottocode: yong1288; code iso 639-3 le plus proche : nru). L’archive contient un sous-ensemble du corpus na de la collection Pangloss : c’est un corpus monolocuteur, constitué de l’intégralité des ressources audio transcrites pour la locutrice principale de ce corpus (Mme LATAMI Dashilame).<br> Le corpus est versionné, de sorte que les expériences menées sur ces ressources (pour la linguistique ou pour le Traitement automatique des langues) soient reproductibles de façon exacte (en pensant bien à joindre l’algorithme : paramètres, répartitions des fichiers dans les différents ensembles, etc.). Toutes les informations pertinentes se trouvent dans les fichiers YAML (extension .yml ; un en français, un autre en anglais).<br> Le sous-dossier des données contient d’une part les audios convertis et démultiplexés et d’autre part les annotations associées à chaque canal desdits audios.<br> Les fichiers récapitulatifs contiennent notamment la liste des graphèmes utilisés dans cette langue (les graphèmes complexes sont particulièrement importants), ainsi que des informations sur les différentes ressources (audios et annotations), comme les identifiants (DOI), les liens vers les fichiers originaux, etc.<br> Au plan informatique, la liste des identifiants DOI des audios et annotations décrits dans ce fichier YAML suffit pour générer ce corpus à un instant t. Un corpus comme celui-ci peut être vu comme la version à l’instant t d’un ensemble de documents de la collection Pangloss : un corpus arrêté à une version précise.<br> Pour plus de précisions : <a href="https://gitlab.com/lacito/outilspangloss">https://gitlab.com/lacito/outilspangloss</a></p>
ENST-Drums: an extensive audio-visual database for drum signals processing
<p>The <strong>ENST-Drums database</strong> is a large and varied research database for automatic drum transcription and processing:</p> <ul> <li>Three professional drummers specialized in different music genres were recorded.</li> <li>Total duration of audio material recorded per drummer is around 75 minutes.</li> <li>Each drummer played his own drum kit.</li> <li>Each sequence used either sticks, rods, brushes or mallets to increase the diversity of drum sounds.</li> <li>The drum kits themselves are varied, ranging from a small, portable, kit with two toms and 2 cymbals, suitable for jazz and latin music ; to a larger rock drum set with 4 toms and 5 cymbals.</li> </ul> <p>Each sequence is recorded on 8 individual audio channels, is filmed from two angles, and is fully annotated</p> <p>A large part of ENST-Drums is publicly available <strong>under some conditions</strong>. These conditions include:</p> <ul> <li>The use and exploitation of the database should be limited to <strong>research</strong> purposes. No commercial use is possible.</li> <li>The database is distributed under the licence "Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)"</li> <li>Any document describing a research work where ENST-Drums was used should include a reference to ENST-Drums and to the paper <em>Olivier Gillet and Gaël Richard. ENST-Drums: an extensive audio-visual database for drum signals processing, In Proc of ISMIR'06, Victoria, Canada, 2006.</em></li> </ul> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We would like to thank:</p> <ul> <li>The 3 drummers: Louis Cavé, Bertrand Clouard and Frédéric Rottier.</li> <li>E. Thiévon (author) and Play Music Publishing (publisher) for the background accompaniment sequences.</li> </ul> <p>The authors wish to acknowledge the support of the French ministry of research (<a href="http://recherche.ircam.fr/equipes/analyse-synthese/musicdiscover">ACI-MusicDiscover</a> project) and of the European Commission under the <a href="http://www.k-space.eu/">FP6-027026-K-SPACE</a> contract.</p>
Audio-Visual Analytics Process
<p>Conceptual process of an audio-visual analytics environment: Data is transformed to images and sound for the human analyst who interactively steers the analysis.</p> <p>Created using the <a href="https://thenounproject.com/icon/engineer-1362184/">"engineer" icon by Pawinee E. from the Noun Project</a> under <a href="https://creativecommons.org/licenses/by/2.0/">CC BY</a>.</p> <p> </p>
Porcelain process raw videos audio files
<p>Porcelain process raw videos audio files</p>
Audio processing and editing videos
<p>List of audio processing and editing videos</p> <p>Videos are in Spanish with test questions during reproduction</p>
Japhug for Natural Language Processing: a single-speaker audio corpus with transcriptions
<p><em>(français ci-dessous)</em></p> <p>This archive contains a dataset (audio files and transcriptions) of a minority language, Japhug (Glottocode: japh1234; closest iso 639-3 code: jya). The archive contains a subset of the Japhug corpus of the Pangloss Collection: it is a single-speaker corpus, consisting of all the audio resources transcribed, for the main speaker of this corpus (Ms. Tshendzin).<br> The corpus is versioned, so that the experiments carried out on these resources (for linguistic research or for Natural Language Processing) are fully reproducible. All relevant information is contained in YAML files (.yml extension; one in French, one in English).<br> The data sub-folder contains the converted and demultiplexed audio files, as well as the annotations associated with each channel of the audio files.<br> The summary files contain, among other things, the list of graphemes used in the language (complex graphemes are particularly important), as well as information on the various resources (audio and annotations), such as their identifiers (DOIs) and links to the original files.<br> From a computational point of view, the list of DOIs of the audios and annotations described in this YAML file is sufficient to generate this corpus at a given time. A corpus like the present one can be viewed as the version, at a given time, of a set of documents in the Pangloss collection: a corpus as it stands at a precise version.</p> <p>Further information is available from <a href="https://gitlab.com/lacito/outilspangloss">https://gitlab.com/lacito/outilspangloss</a></p> <p>---------------</p> <p>Cette archive contient un jeu de données (audios et transcriptions) d’une langue à tradition orale, le japhug (Glottocode: japh1234; code iso 639-3 le plus proche : jya). L’archive contient un sous-ensemble du corpus japhug de la collection Pangloss : c’est un corpus monolocuteur, constitué de l’intégralité des ressources audio transcrites pour la locutrice principale de ce corpus (Mme Tshendzin).<br> Le corpus est versionné, de sorte que les expériences menées sur ces ressources (pour la linguistique ou pour le Traitement automatique des langues) soient reproductibles de façon exacte (en pensant bien à joindre l’algorithme : paramètres, répartitions des fichiers dans les différents ensembles, etc.). Toutes les informations pertinentes se trouvent dans les fichiers YAML (extension .yml ; un en français, un autre en anglais).<br> Le sous-dossier des données contient d’une part les audios convertis et démultiplexés et d’autre part les annotations associées à chaque canal desdits audios.<br> Les fichiers récapitulatifs contiennent notamment la liste des graphèmes utilisés dans cette langue (les graphèmes complexes sont particulièrement importants), ainsi que des informations sur les différentes ressources (audios et annotations), comme les identifiants (DOI), les liens vers les fichiers originaux, etc.<br> Au plan informatique, la liste des identifiants DOI des audios et annotations décrits dans ce fichier YAML suffit pour générer ce corpus à un instant t. Un corpus comme celui-ci peut être vu comme la version à l’instant t d’un ensemble de documents de la collection Pangloss : un corpus arrêté à une version précise.<br> Pour plus de précisions : <a href="https://gitlab.com/lacito/outilspangloss">https://gitlab.com/lacito/outilspangloss</a></p>
Fig. 2 in A mesh network case study for digital audio signal processing in Smart Farm
Fig. 2. IoT node prototype.
Fig. 6. T3 in A mesh network case study for digital audio signal processing in Smart Farm
Fig. 6. T3 test results.
Fig. 4 in A mesh network case study for digital audio signal processing in Smart Farm
Fig. 4. Organization diagram of tests T1 (left above), T2 (left below) and T3 (right).
Fig. 7. Packets received during testing using 4 in A mesh network case study for digital audio signal processing in Smart Farm
Fig. 7. Packets received during testing using 4 nodes.
Fig. 1 in A mesh network case study for digital audio signal processing in Smart Farm
Fig. 1. Mesh network topology (left) and star topology (right).
Model weights and pre-processed audio files for Diffiner
<p>This repository contains the pre-trained model weights for Diffiner proposed in the paper <a href="https://arxiv.org/abs/2210.17287">Diffiner: A Versatile Diffusion-based Generative Refiner for Speech Enhancement</a> by Sony. Furthermore, to run Diffiner same as our paper, the preceding results processed by DCUnet are provided here.</p> <p>More information about Diffiner including our codes is available in <a href="https://github.com/sony/diffiner">https://github.com/sony/diffiner.</a></p>
Fig. 5 in A mesh network case study for digital audio signal processing in Smart Farm
Fig. 5. Results in T1 (left) and T2 (right).
Fig. 3 in A mesh network case study for digital audio signal processing in Smart Farm
Fig. 3. Diagram of the proposed algorithm..
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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International Brain Laboratory public data
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OpenNeuro
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