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17 results for “EDMED”
Beatport EDM Key Dataset
<p><em>The Beatport EDM Key Dataset</em> includes 1,486 two-minute sound excerpts from various EDM subgenres, annotated with single-key labels, comments and confidence levels generously provided by Eduard Mas Marín, and thoroughly revised and expanded by Ángel Faraldo.</p> <p>The original audio samples belong to online audio snippets from Beatport, an online music store for DJ's and Electronic Dance Music Producers (<http:\\www.beatport.com>). If this dataset were used in further research, we would appreciate the citation of the current DOI (10.5281/zenodo.1101082) and the following doctoral dissertation, where a detailed description of the properties of this dataset can be found:</p> <p>Ángel Faraldo (2017). <em>Tonality Estimation in Electronic Dance Music: A Computational and Musically Informed Examination.</em> PhD Thesis. Universitat Pompeu Fabra, Barcelona.</p> <p>This dataset is mainly intended to assess the performance of computational key estimation algorithms in electronic dance music subgenres.</p>
Edme Bernard Auguste Daniel (d1394)
<b>-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> --</b><br><br><u>Name</u>: Edme Bernard Auguste Daniel<br><u>musiXplora-ID</u>: d1394<br><u>musiXplora-URI</u>: <a href="https://musixplora.de/mxp/d1394">https://musixplora.de/mxp/d1394</a><br><u>Gender</u>: m<br><u>Date of Birth</u>: 12 May 1829<br><u>Place of Birth</u>: Marseille<br><u>Date of Death</u>: 1873<br><u>Place of Death</u>: Marseille<br><u>First Mentioned</u>: 1854<br><u>Sectors</u>: Instrumentenbau<br><u>Professions (Musical)</u>: Holzblasinstrumentenbauer, Instrumentenbauer<br><u>Other Places of Activity</u>: Marseille<br><br><br><u>Institutionen:</u><br><table><tbody><tr><th>Role</th><th>Title</th><th>mXp-ID</th></tr><tr><td>Related</td><td>Familie Barbet Granier Daniel Palanque Diter</td><td><a href="https://musixplora.de/mxp/3030480">3030480</a></td></tr></tbody></table><br><u>Ereignisse:</u><br><table><tbody><tr><th>Role</th><th>Title</th><th>mXp-ID</th></tr><tr><td>Hersteller</td><td></td><td><a href="https://musixplora.de/mxp/Herstellung">Herstellung</a></td><td>6021029</td></tr></tbody></table><br><br><u>Changelog</u>:<br> - v0.0.1: Initial Upload.<br>
WaivOps EDM-HSE: Open Audio Resources for Machine Learning in Music
<p><strong>EDM-HSE Dataset</strong></p> <p>EDM-HSE is an open audio dataset containing a collection of code-generated drum recordings in the style of modern electronic house music. It includes 8,000 audio loops recorded in uncompressed stereo WAV format, created using custom audio samples and a MIDI drum dataset. The dataset also comes with paired JSON files containing MIDI note numbers (pitch) and tempo data, intended for supervised training of generative AI audio models.</p> <p><strong>Overview</strong></p> <p>The EDM-HSE Dataset was developed using an algorithmic framework to generate probable drum notations commonly played by EDM music producers. For supervised training with labeled data, a variational mixing technique was applied to the rendered audio files. This method systematically includes or excludes drum notes, assisting the model in recognizing patterns and relationships between drum instruments, thereby enhancing its generalization capabilities.</p> <p>The primary purpose of this dataset is to provide accessible content for machine learning applications in music and audio. Potential use cases include generative music, feature extraction, tempo detection, audio classification, rhythm analysis, drum synthesis, music information retrieval (MIR), sound design and signal processing.</p> <p><strong>Specifications</strong></p> <ul> <li>8,000 audio loops (approximately 17 hours)</li> <li>16-bit WAV format</li> <li>Tempo range: 120–130 BPM</li> <li>Paired label data (WAV + JSON)</li> <li>Variational drum patterns</li> <li>Subgenre styles (Big room, electro, minimal, classic)</li> </ul> <p>A JSON file is provided for referencing and converting MIDI note numbers to text labels. You can update the text labels to suit your preferences.</p> <p><strong>License</strong></p> <p>This dataset was compiled by WaivOps, a crowdsourced music project managed by the sound label company Patchbanks. All recordings have been compiled by verified sources for copyright clearance.</p> <p>The EDM-HSE dataset is licensed under Creative Commons Attribution 4.0 International <a href="https://creativecommons.org/licenses/by/4.0/">(CC BY 4.0)</a>.</p> <p><strong>Additional Info</strong></p> <p>Please note that this dataset has not been fully reviewed and may contain minor notational errors or audio defects.</p> <p>For audio examples or more information about this dataset, please refer to the <a href="https://github.com/patchbanks/WaivOps-EDM-HSE">GitHub repository</a>.</p>
Dataset for the article EDM-GNSS distance comparison at the EURO5000 calibration baseline: preliminary results
<p>GNSS RINEX observation files for the observation campaign used in the article "EDM-GNSS distance comparison at the EURO5000 calibration baseline: preliminary results" by Kinga Wezka, Luis García-Asenjo, Dominik Próchniewicz, Sergio Baselga, Ryszard Szpunar, Pascual Garrigues, Janusz Walo and Raquel Luján, Journal of Applied Geodesy https://doi.org/10.1515/jag-2022-0049. The work leading to this paper was performed within the 18SIB01 GeoMetre project of the European Metrology Programme for Innovation and Research (EMPIR). This project has received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme, funder ID: 10.13039/100014132. Raquel Luján acknowledges the funding from the Programa de Ayudas de Investigación y Desarrollo (PAID-01-20) de la Universitat Politècnica de València.</p>
WaivOps EDM-TR9: Open Audio Resources for Machine Learning in Music
<p><strong>EDM-TR9 Dataset</strong></p> <p>EDM-TR9 is an open audio dataset composed of a series of drum recordings in the style of electronic dance music (EDM). This dataset primarily focuses on the distinctive sounds and rhythm patterns of the Roland TR-909 drum machine within the subgenres of dance, house and techno music. The dataset contains 3780 audio loops recorded in uncompressed stereo WAV format, produced with custom drum samples and MIDI-programmed rhythms at various tempo rates.</p> <p><strong>Dataset</strong></p> <p>The primary objective of this dataset is to provide accessible content for machine learning applications in music and audio research. Some potential use cases for this dataset include tempo detection and classification, drum rhythm analysis, audio-to-MIDI conversion, source separation, automated mixing, music information retrieval, AI music generation, sound design and signal processing.</p> <p><strong>Specifications</strong></p> <ul> <li>3780 audio loops (approximately 8 hours)</li> <li>24-bit WAV format</li> <li>BPM labeled</li> <li>Tempo range: 120–140bpm</li> <li>Variational drum patterns</li> <li>EDM drum rhythms</li> </ul> <p><strong>License</strong></p> <p>This dataset was compiled by WaivOps, a crowdsourced music project managed by the sound label company Patchbanks. All recordings have been compiled by verified sources for copyright clearance.</p> <p>The EDM-TR9 dataset is licensed under Creative Commons Attribution 4.0 International <a href="https://creativecommons.org/licenses/by/4.0/">(CC BY 4.0)</a>.</p> <p><strong>Additional Info</strong></p> <p>For audio examples or more information about this dataset, please refer to the <a href="https://github.com/patchbanks/WaivOps-EDM-TR9">GitHub repository</a>.</p>
WaivOps EDM-TR8: Open Audio Resources for Machine Learning in Music
<p><strong>EDM-TR8 Dataset</strong></p> <p>EDM-TR8 is an open audio dataset composed of a series of drum recordings in the style of electronic dance music (EDM). This dataset primarily focuses on the iconic sounds of the Roland TR-808 drum machine with additional electro synth drums. The dataset contains 3,790 audio loops recorded in uncompressed stereo WAV format, generated with custom audio samples and a MIDI dataset used for training symbolic music models.</p> <p><strong>Dataset</strong></p> <p>The primary objective of this dataset is to provide accessible content for machine learning applications in music and audio research. Some potential use cases for this dataset include tempo detection and classification, drum rhythm analysis, audio-to-MIDI conversion, source separation, automated mixing, music information retrieval, AI music generation, sound design and signal processing.</p> <p><strong>Specifications</strong></p> <ul> <li>3790 audio loops (approximately 9 hours)</li> <li>16-bit WAV format</li> <li>BPM labeled</li> <li>Tempo range: 95–130bpm</li> <li>Variational drum patterns</li> <li>Multi-genre rhythm styles</li> </ul> <p><strong>License</strong></p> <p>This dataset was compiled by WaivOps, a crowdsourced music project managed by the sound label company Patchbanks.. All recordings have been compiled by verified sources for copyright clearance.</p> <p>The EDM-TR8 dataset is licensed under Creative Commons Attribution 4.0 International <a href="https://creativecommons.org/licenses/by/4.0/">(CC BY 4.0)</a>.</p> <p><strong>Additional Info</strong></p> <p>For audio examples or more information about this dataset, please refer to the <a href="https://github.com/patchbanks/WaivOps-EDM-TR8">GitHub repository</a>.</p>
GiantSteps+ EDM Key Dataset
<p><em>The GiantSteps+ EDM Key Dataset</em> includes 600 two-minute sound excerpts from various EDM subgenres, annotated with single-key labels. This dataset focus in problematic Beatport excerpts, so it is biased, but it is interesting to test the robustness of key recognition systems. These 600 tracks have been analysed by Daniel G. Camhi and Ángel Faraldo, providing pitch-class set descriptions, key and modal changes, comments and confidence levels for the individual tracks.</p> <p>This dataset is a revision of the original GiantSteps Key Dataset, available in Github (<https://github.com/GiantSteps/giantsteps-key-dataset>) and initially described in:</p> <p>Knees, P., Faraldo, Á., Herrera, P., Vogl, R., Böck, S., Hörschläger, F., Le Goff, M. (2015). Two Datasets for Tempo Estimation and Key Detection in Electronic Dance Music Annotated from User Corrections. In <em>Proceedings of the 16th International Society for Music Information Retrieval Conference</em>, 364–370. Málaga, Spain.</p> <p>The original audio samples belong to online audio snippets from Beatport, an online music store for DJ's and Electronic Dance Music Producers (<http:\\www.beatport.com>). If this dataset were used in further research, we would appreciate the citation of the current DOI (10.5281/zenodo.1101082) and the following doctoral dissertation, where a detailed description of the properties of this dataset can be found:</p> <p>Ángel Faraldo (2017). <em>Tonality Estimation in Electronic Dance Music: A Computational and Musically Informed Examination.</em> PhD Thesis. Universitat Pompeu Fabra, Barcelona.</p>
Electronic Distance Measurements (EDM) on active Sicilian Volcanoes (1975-2009).
<p><strong>File descriptions</strong>: This dataset includes ca. 8000 Electronic Distance Measurements (EDM) collected from 1974 to 2009 on several networks installed on Sicilian Volcanoes (Vulcano, Stromboli, Etna and Pantelleria).<br> EDM represent one of the first methods to detect ground deformation on volcanoes having been used since 1964 on Kilauea (Hawaii). It is a precise technique that uses a laser to measure the transit time of light between a base station to the reflecting prisms positioned around the volcano; the repetition of these measurements in time allows one to monitor the slope distance variations.<br> EDM is a powerful tool for volcano monitoring that has been useful to define the features of magmatic and hydrothermal sources as their volume, position, geometry, and dynamics. Moreover, this technique has been largely used on volcanoes in 70th to 90th years until the 2000s when it has been gradually abandoned in favor of GPS.<br> This dataset reports data obtained on several Sicilian volcanoes (Etna, Vulcano, Stromboli and Pantelleria) from the early ’70 to until the 2000s, in which EDM measurements have played a major role in volcanic process knowledge and that make the Sicilian volcanoes among the ones with the longest geodetic record in the World.<br> Database has been organized on Microsoft Excel files and each file reports the network name (the networks list is in Table1.xls).<br> Inside each data file (in “database” directory), there are two sheets with a sheet reporting all measured distances and a table with associated errors. The position of each benchmark is reported on specific coordinate network files in “benchmarks coordinates” directory). All the coordinates have been obtained with GPS technique, except for the Ionica network where they have been derived from paper maps and therefore suffer by a bigger approximation.<br> In the data files, rows show the measurement date, the instrument model and recorded values; in the first column, the pairs of benchmarks considered are reported.<br> The networks Etna S and Etna NE have additional files, reporting the most frequent measurements taken during the eruptive crises of July 2001 (onset of 2001 eruption) and of October 2002 (onset of the 2002-03 eruption).<br> Almost all networks were measured using both the AGA6BL and the AGA 6000 Geodimeters. In the files, the survey in which the transition from one instrument to the other took place is highlighted. In those surveys, measurements were performed with both instruments in order to maintain the time-series continuity. Furthermore, in the tables “double” measurements related also to reflectors changes or new benchmarks have been highlighted.</p>
DE, Extended Sensors, EDM-enabled extended sensors with surround view generation
<p>Use Case Category: <strong>Extended Sensors</strong><br> User Story: <strong>EDM-enabled extended sensors with surround view generation</strong><br> Location: German (DE) trial site</p> <p>According to 3GPP TS 22.186 R16, Extended Sensors “enable the exchange of raw or processed data gathered through local sensors or live video data among vehicles, RSUs, devices of pedestrians and V2X application servers. The vehicles can enhance the perception of their environment beyond what their own sensors can detect and have a more holistic view of the local situation”.</p> <p>User Story: <strong>EDM-enabled extended sensors with surround view generation</strong></p> <p>The objective of this user story is to share LDM data and raw sensor data for real-time prediction and planning tasks made possible by 5G technology. More precisely, the use case deals with a situation when the perception obtained by the on-board sensors is not enough and needs to be enhanced by sensor data from other traffic participants.</p> <p>The user story contains several connected vehicles equipped with sensors as well as roadside infrastructure comprising sensors and edge computing infrastructure (eRSU). The vehicles and the eRSU using their respective sensor data build their individual situational awareness, identifying objects, lane markings or the road condition to support their prediction and planning functions. However, each individual vehicle’s sensors as well as roadside sensors are limited in the perception in different ways. The sensors view could be obstructed by objects, limited by weather conditions, or not covering a specific area. To mitigate the lack of environment information, vehicles share extracts ROIs (regions of interest) from their LDMs and/or sensor raw data and the eRSU shares its Edged Dynamic Map (EDM).</p> <p>In the proposed setup, the eRSU assisted map update is valid within the coverage area of the eRSU. Cars not within the coverage area are relying on updates of their respective eRSU and their neighbouring vehicles. To assist cars moving from one coverage area to another, the future eRSU’s EDM will be provided. Neighbouring eRSUs exchange their EDMs to provide the vehicles with map information when approaching a new coverage area.</p> <p>The storyline of the use case goes like this. There are two connected autonomous cars driving on the same lane. The two cars are sending relevant LDM data to the corresponding eRSU where the EDM is updated. Suddenly, there is an unexpected event that makes the first car brake and start a lane changing manoeuvre. The event can be for instance a vehicle that stops and blocks the lane. This sudden action is propagated to the rest of the cars that perceive that something is happening. The two connected and automated cars request the EDM to the eRSU under their coverage and they fuse it with their LDM to analyse the situation. They determine that a lane changing manoeuvre is necessary as the lane is blocked some meters ahead. Using the collected information, they start planning and executing the manoeuvre. The EDM contains only processed lightweight data of traffic participants (mainly position, heading, size and speed) that is sufficient for rapid risk estimation and decision making but it is not enough to create a 360º surround view. To favour a quick decision the eRSU provide to each vehicle a filtered EDM with the relevant items for the ROI of the vehicle, filtering any irrelevant data for the vehicle’s path. The rear vehicle has its field of view severely restricted and determines that a surround view generation would help keeping the driver in the loop and decreasing the risk of the lane changing manoeuvre. The leading vehicle has better visibility and the do not require a surround view. Consulting the EDM, the rear vehicle selects to which vehicles it needs to request raw sensor data to enhance its field of view. The vehicle generates a 360º surround view by fusing onboard sensors (cameras and Lidar) and data (video and Lidar’s 3D cloud) coming from the selected traffic participants. This is done by direct Vehicle to Vehicle (V2V) communication. According to the processing capacity of data source and destination and the network performance between V2V communication participants, the data origin vehicle generates data streams with an appropriate resolution and bitrate. Furthermore, the eRSU provides tokens to be used to perform secure data transfer between the vehicles.</p>
Replication Data for: EDM
<p>Included are preprocessed data files for the QM9 and GEOM-Drugs datasets accompanying the Equivariant Diffusion Models (EDM) repository by Hoogeboom et al. 2022.</p>
Active Corrosion Control in Wire EDM of carbide punches
<p>The electrical field within the machining area of a WEDM machine was simulated using COMSOL Multiphysics software. In this document, we share the results of this simulation, in particular</p> <ul> <li>the geometry considered</li> <li>Figure 5: Electrical field distribution while machining a) with negative polarity of -10 V, b) with positive polarity of +5 V and c) with positive polarity of +2 V</li> <li>Figure 6: Worktank geometry considered and electrical field distribution during storage with an external applied voltage of -0.5 V. 1) when the WC-Co workpiece is connected to the machine ground with: a) brass wire also connected to the ground and b) wire removed. 2) when the WC-Co workpiece is insulated (floating ground) with the c) wire connected to the ground, d) wire at floating ground and e) wire removed.</li> </ul>
Neo-adjuvant Pembrolizumab in dMMR/ POLE-EDM Uterine Cancer Patients: a Feasibility Study
ClinicalTrials.gov study NCT04262089. IPD Sharing: NO. Countries: 1. Publications: 1.
Figure 3 from: Guralnick RP, Cellinese N, Deck J, Pyle RL, Kunze J, Penev L, Walls R, Hagedorn G, Agosti D, Wieczorek J, Catapano T, Page EDM (2015) Community Next Steps for Making Globally Unique Identifiers Work for Biocollections Data. ZooKeys 494: 133-154. https://doi.org/10.3897/zookeys.494.9352
Figure 3 - Identifier schemes differ in whether redirections and mappings to ensure stability are centrally managed or not. Top: a DOI dereferencing service like CrossRef or Datacite redirects to the actual content provider; the URIs of content data and RDF metadata are publicly visible and can be used as independent (albeit often unstable) identifiers. Bottom: A linked open data pattern, where each content provider assumes the responsibility for maintaining a stable mapping; the content negotiation is internal. Modified after Hagedorn 2013.
Figure 2 from: Guralnick RP, Cellinese N, Deck J, Pyle RL, Kunze J, Penev L, Walls R, Hagedorn G, Agosti D, Wieczorek J, Catapano T, Page EDM (2015) Community Next Steps for Making Globally Unique Identifiers Work for Biocollections Data. ZooKeys 494: 133-154. https://doi.org/10.3897/zookeys.494.9352
Figure 2 - Example of a PURL-URI as a QR-Code, in this example attached to a digitised lichen type specimen in the Natural History Museum, University of Oslo. The QR-Code corresponds to http://purl.org/nhmuio/id/c1a8b878-a4f9-448b-be00-26cbad58b11c.
Figure 1 from: Guralnick RP, Cellinese N, Deck J, Pyle RL, Kunze J, Penev L, Walls R, Hagedorn G, Agosti D, Wieczorek J, Catapano T, Page EDM (2015) Community Next Steps for Making Globally Unique Identifiers Work for Biocollections Data. ZooKeys 494: 133-154. https://doi.org/10.3897/zookeys.494.9352
Figure 1 - Example of UUIDs embedded within QR-Codes on microcentrifuge tube labels. The 5 mm × 5 mm QR-Codes (Version 2) are printed with a standard laser printer on sheets of self-adhesive 9 mm dots, and scan reliably with a standard barcode reader, while still providing room for a human-readable 5-character prefix + 5-digit number (the human-readable number and UUID are permanently cross-linked in the data management system). Photo: Robert K. Whitton.
Effect of TruNatomy and HyFlex EDM Instrumentation on Postoperative Pain
ClinicalTrials.gov study NCT05289973. IPD Sharing: Not stated. Countries: 0. Publications: 3.
EDM-Dock-Dataset
<p>Training dataset for EDM-Dock. See <em>Deep Learning Model for Flexible and Efficient Protein-Ligand Docking</em> by Masters et al. for a complete description of the dataset.</p>
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