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106 results for “drum”
kac_drumset: A Dataset Generator for Arbitrarily Shaped Drums
<p>This publication documents the various datasets generated using the kac_drumset codebase. The aims of kac_drumset is to provide a robust framework for the generation and analysis of arbitrarily shaped drums. The source code for this project is available here: <a href="https://github.com/lewiswolf/kac_drumset">https://github.com/lewiswolf/kac_drumset</a>.</p> <p><strong>Background</strong></p> <p>Arbitrarily shaped drums are a strange family of percussion instruments and a wholly meta-physical construction in this contemporary setting. These percussive instruments possess a number of interesting musical characteristics resulting from their particular geometric designs. As it currently stands, these instruments remain largely unexplored throughout musical practice, as they were originally devised as a collection of hypothetical mathematical objects. These datasets serve to sonify these objects so as to explore these conceptual constructions in the audio domain.</p> <p><strong>Usage</strong></p> <p>To use these datasets, first install kac_drumset:</p> <pre><code class="language-bash">pip install "git+https://github.com/lewiswolf/kac_drumset.git#egg=kac_drumset"</code></pre> <p>And then in python:</p> <pre><code class="language-python">from kac_drumset import ( # methods loadDataset, transformDataset, # classes TorchDataset, ) dataset: TorchDataset = transformDataset( # load a dataset (any folder containing a metadata.json) loadDataset('absolute/path/to/data'), # alter the dataset representation, either as an end2end, fft or mel. {'output_type': 'end2end'}, ) # use the dataset for i in range(dataset.__len__()): x, y = dataset.__getitem__(i) ...</code></pre> <p>For more details on using kac_drumset, see <a href="https://github.com/lewiswolf/kac_drumset/blob/master/readme.md">the project's documentation</a>.</p> <p><strong>2000 Convex Polygonal Drums of Varying Size</strong></p> <p>Each sample in this dataset corresponds to a randomly generated convex polygon. The audio for each sample was generated using a two-dimensional physical model of a drum. Each sample is one second long and decays linearly.</p> <p>Contained in this dataset are ten different sizes of drums - 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5, 0.6 - each of which is a measure of the longest vertex of each drum in meters. There are 40 different drums sampled for each size. Each drum is sampled five times, first by being struck in the geometric centroid, and then by being struck four more times in random locations. This dataset is labelled with the vertices of each polygon, normalised to the unit interval, and the strike location of each sample.</p> <p>The audio is sampled at 48khz, and the default representation is raw audio. Each sample is stored in the metadata.json, alongside being made available audibly as a 24-bit .wav and graphically as a .png.</p> <p><strong>5000 Circular Drums of Varying Size</strong></p> <p>Each sample in this dataset corresponds to a randomly generated circular drum. The audio for each sample was generated using additive synthesis, inferred using a closed form solution to the two dimensional wave equation. Each sample is one second long and decays exponentially.</p> <p>Contained in this dataset are 1000 different drums, each determined by a randomly generated size (0.1, 2.0) in meters. Each drum is sampled five times, first being struck in the geometric centroid, and then by being struck four more times in random locations. This dataset is labelled with the size of each drum and the strike location of each sample.</p> <p>The audio is sampled at 48khz, and the default representation is raw audio. Each sample is stored in the metadata.json, alongside being made available audibly as a 24-bit .wav and graphically as a .png.</p> <p><strong>5000 Rectangular Drums of Varying Dimension</strong></p> <p>Each sample in this dataset corresponds to a randomly generated rectangular drum. The audio for each sample was generated using additive synthesis, inferred using a closed form solution to the two dimensional wave equation. Each sample is one second long and decays exponentially.</p> <p>Contained in this dataset are 1000 different drums, each determined by a randomly generated size (0.1, 2.0) in meters and aspect ratio (0.25, 4.0). Each drum is sampled five times, first being struck in the geometric centroid, and then by being struck four more times in random locations. This dataset is labelled with the size and aspect ratio of each drum, and the strike location of each sample.</p> <p>The audio is sampled at 48khz, and the default representation is raw audio. Each sample is stored in the metadata.json, alongside being made available audibly as a 24-bit .wav and graphically as a .png.</p>
Data from: Dynamics of diet-egg transfer of fatty acids in the teleost fish, red drum (Sciaenops ocellatus)
<p>Eggs of marine organisms are increasingly being recognized as important components of marine food webs. The degree to which egg fatty acid profiles reflect maternal diet fatty acid profiles, and therefore the value of fatty acids in eggs as trophic biomarkers, depends on the species' reproductive strategy and the extent of modification of ingested fatty acids. We measured the dynamics of transfer of recently ingested fatty acids to spawned eggs in a batch-spawning teleost, red drum (<em>Sciaenops ocellatus</em>). Results of 21 dietshift experiments, from which the fatty acid profiles of the diets and eggs were compared, showed that 15 of 27 fatty acids measured (one saturated, two monounsaturated and 12 polyunsaturated fatty acids) in eggs were correlated with their levels in the recent diet, and the rate of incorporation into eggs was proportional to the magnitude of the diet shift. Large shifts in diet might occur naturally during spawningmigrations or when prey communities vary over time. Results of this study indicate that fatty acids in red drum eggs can be useful for studying adult diet and exploring trophic linkages in marine systems.</p> <p>This article is part of the theme issue 'The next horizons for lipids as 'trophic biomarkers': evidence and significance of consumer modification of dietary fatty acids'.</p>
Vocal drum sounds in Human Beatboxing: an acoustic and articulatory exploration using electromagnetic articulography
<p>This dataset constitutes the supplementary material of a paper in review in the Journal of the Acoustical Society of America (JASA)</p>
StemGMD: A Large-Scale Audio Dataset of Isolated Drum Stems for Deep Drums Demixing - part 1
<p>We introduce StemGMD, a new large-scale dataset of isolated drum stems that builds upon the extensive MIDI collection found in <a href="https://magenta.tensorflow.org/datasets/groove">Magenta's Groove MIDI Dataset (GMD)</a>.</p> <p>GMD is a 13.6-hour corpus of expressive drum performances executed by ten drummers on a Roland TD-11 electronic drum kit. It contains 1150 MIDI files along with the corresponding full-kit audio mixtures.</p> <p>As a first step in creating StemGMD, we mapped the 22 different MIDI pitches found in the original files onto nine canonical instruments through the reduction scheme proposed in J. Gillick, A. Roberts, J. Engel, D. Eck, and D. Bamman, "Learning to groove with inverse sequence transformations," in International Conference on Machine Learning (ICML), vol. 97, 2019, pp. 2269–2279.</p> <p>Each of the nine resulting MIDI channels was manually synthesized as a 16-bit/44.1 kHz stereo WAV file using ten realistic-sounding acoustic drum kits sourced from the <a href="https://support.apple.com/en-me/guide/logicpro/lgsi2fb2509e/mac">Logic Pro X sample libraries</a>, i.e., Bluebird, Brooklyn, Detroit Garage, East Bay, Heavy, Motown Revisited, Portland, Retro Rock, Roots, and SoCal.</p> <p>As a result, StemGMD contains 1224 hours of audio, which correspond to more than 136 hours of full-kit mixtures. Moreover, StemGMD also contains single hits for each of the drum pieces at ten different velocities ranging from 30 to 127.</p> <p>To the best of our knowledge, StemGMD is the largest publicly available dataset of drums to date. Moreover, it is the first collection of single-instrument clips from all nine pieces in a canonical drum kit, making it well-suited for training deep drums demixing models.</p> <p> </p> <p><strong>*** THIS IS PART 1 OF 2 ***</strong></p> <p><strong>Download part 2 here:</strong> <a href="../records/7882857">https://zenodo.org/records/7882857 </a> (now available!) </p> <p>After downloading both parts, run <strong>unzip_StemGMD.sh</strong> to build the dataset from the split archive files.<br>Once unzipped, StemGMD will take just over <strong>1.13 TB </strong>of memory.</p> <p> </p> <p>The dataset is made available under a <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International (CC BY 4.0) License</a>.</p> <p>____________________________</p> <p>We employed the dataset in our paper titled "Toward Deep Drum Source Separation," published in Pattern Recognition Letters. </p> <div> <div> <div> <div> <p>Please, cite this work as: A. I. Mezza, R. Giampiccolo, A. Bernardini, and A. Sarti, "Toward Deep Drum Source Separation," Pattern Recognition Letters, vol. 183, pp. 86-91, 2024, doi: 10.1016/j.patrec.2024.04.026.</p> <pre>@article{mezza2024, title = {Toward deep drum source separation}, author = {Alessandro Ilic Mezza and Riccardo Giampiccolo and Alberto Bernardini and Augusto Sarti}, journal = {Pattern Recognition Letters}, volume = {183}, pages = {86-91}, year = {2024}, issn = {0167-8655}, doi = {https://doi.org/10.1016/j.patrec.2024.04.026} }</pre> </div> </div> </div> </div>
StemGMD: A Large-Scale Audio Dataset of Isolated Drum Stems for Deep Drums Demixing - part 2
<p>We introduce StemGMD, a new large-scale dataset of isolated drum stems that builds upon the extensive MIDI collection found in <a href="https://magenta.tensorflow.org/datasets/groove">Magenta's Groove MIDI Dataset (GMD)</a>.</p> <p>GMD is a 13.6-hour corpus of expressive drum performances executed by ten drummers on a Roland TD-11 electronic drum kit. It contains 1150 MIDI files along with the corresponding full-kit audio mixtures.</p> <p>As a first step in creating StemGMD, we mapped the 22 different MIDI pitches found in the original files onto nine canonical instruments through the reduction scheme proposed in J. Gillick, A. Roberts, J. Engel, D. Eck, and D. Bamman, "Learning to groove with inverse sequence transformations," in International Conference on Machine Learning (ICML), vol. 97, 2019, pp. 2269–2279.</p> <p>Each of the nine resulting MIDI channels was manually synthesized as a 16-bit/44.1 kHz stereo WAV file using ten realistic-sounding acoustic drum kits sourced from the <a href="https://support.apple.com/en-me/guide/logicpro/lgsi2fb2509e/mac">Logic Pro X sample libraries</a>, i.e., Bluebird, Brooklyn, Detroit Garage, East Bay, Heavy, Motown Revisited, Portland, Retro Rock, Roots, and SoCal.</p> <p>As a result, StemGMD contains 1224 hours of audio, which correspond to more than 136 hours of full-kit mixtures. Moreover, StemGMD also contains single hits for each of the drum pieces at ten different velocities ranging from 30 to 127.</p> <p>To the best of our knowledge, StemGMD is the largest publicly available dataset of drums to date. Moreover, it is the first collection of single-instrument clips from all nine pieces in a canonical drum kit, making it well-suited for training deep drums demixing models.</p> <p> </p> <p><strong>*** THIS IS PART 2 OF 2 ***</strong></p> <p><strong>Download part 1 here:</strong> <a href="../records/7860223">https://zenodo.org/records/7860223 </a></p> <p>After downloading both parts, run <strong>unzip_StemGMD.sh</strong> to build the dataset from the split archive files.<br>Once unzipped, StemGMD will take just over <strong>1.13 TB </strong>of memory.</p> <p> </p> <p>The dataset is made available under a <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International (CC BY 4.0) License</a>.<br><br>_____________________________<br><br>We employed the dataset in our paper titled "Toward Deep Drum Source Separation," published in Pattern Recognition Letters. </p> <div> <div> <div> <div> <p>Please, cite this work as: A. I. Mezza, R. Giampiccolo, A. Bernardini, and A. Sarti, "Toward Deep Drum Source Separation," Pattern Recognition Letters, vol. 183, pp. 86-91, 2024, doi: 10.1016/j.patrec.2024.04.026.</p> <pre>@article{mezza2024, title = {Toward deep drum source separation}, author = {Alessandro Ilic Mezza and Riccardo Giampiccolo and Alberto Bernardini and Augusto Sarti}, journal = {Pattern Recognition Letters}, volume = {183}, pages = {86-91}, year = {2024}, issn = {0167-8655}, doi = {https://doi.org/10.1016/j.patrec.2024.04.026} }</pre> </div> </div> </div> </div>
A visual summary of the three categories of Brutus's drumming communication
<p>[This figure appears in DOI: 10.31234/osf.io/kgqy9 (Version 3). The figure was not included in the published version of the manuscript DOI: 10.1007/s10071-021-01584-3.].</p>
Uruguayan candombe drumming - beat and downbeat tracking data set
<p>Uruguayan Candombe drumming - beat and downbeat data set</p> <p><br>===========<br>Description<br>===========</p> <p>This dataset includes more than 2 hours of Candombe recordings, with annotated beats and downbeats. It features 35 complete performances by renowned players, playing in groups of three to five drums. A total of 26 tambor players from various generations took part, representing the three key traditional Candombe styles. These recordings were created in a studio setting over a period of more than two decades as part of musicological research. The sessions from 1992 and 1995 were produced by Luis Jure, and the session from 2014 was produced by Luis Jure and Martín Rocamora.</p> <p>The audio files are in stereo with a sampling rate of 44.1 kHz and 16-bit precision. An expert annotated the location of beats and downbeats, totaling more than 4700 downbeats. The audio is provided in flac format and the annotations are in .csv files. The values in the first column of the CSV file represent the time instants of the beats. The numbers in the second column indicate both the bar number and the beat number within the bar. For example, 1.1, 1.2, 1.3, and 1.4 represent the four beats of the first bar. Therefore, each label ending in .1 indicates a downbeat. Another set of annotations is provided as .beats files in which the bar numbers are removed.</p> <p><br>========<br>Citation<br>========</p> <p>This dataset was released with the publication of the following paper. So if you find the dataset useful and want to reference it in your publications, please cite it.</p> <p>"Beat and Downbeat Tracking Based on Rhythmic Patterns Applied to the Uruguayan Candombe Drumming”. Leonardo Nunes, Martín Rocamora, Luis Jure, Luiz W. P. Biscainho. Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR 2015), pages 264-270, Málaga, Spain, 26-30 October, 2015.</p> <p>@inproceedings{Nunes2015,<br> author = {Leonardo Nunes and Martín Rocamora and Luis Jure and Luiz W. P. Biscainho},<br> title = {{Beat and Downbeat Tracking Based on Rhythmic Patterns Applied to the Uruguayan Candombe Drumming}},<br> booktitle = {Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR 2015)},<br> month = {Oct.},<br> address = {Málaga, Spain},<br> pages = {264--270},<br> year = {2015}<br>}</p> <p><br>========<br>Candombe<br>========</p> <p>Candombe is a vital part of Uruguayan popular culture, with thousands of practitioners and its rhythm influencing various genres of popular music. In 2009, UNESCO recognized it as part of the Intangible Cultural Heritage of Humanity. While it originated in Uruguay, Candombe has its roots in the culture brought by African slaves in the 18th century. Over time, it has evolved to incorporate the descendants of European immigrants and has become a part of the entire society. Candombe drumming, with its unique rhythm, is the essential element of this tradition, which also includes dancing, symbolic characters, and costumes.</p> <p>The drum used in Candombe is called tambor, which is Spanish for "drum." There are three different sizes: chico (small), repique (medium), and piano (big). Each size has its own unique sound, ranging from high to low frequency, and its own specific rhythmic patterns. All three drums are played with a stick in the dominant hand and the other one hitting the skin directly. The stick is also used to hit the shell when playing the clave or madera pattern. A minimal ensemble of drums (cuerda de tambores) must have at least one of each of the three drums. During a llamada de tambores, the ensemble usually consists of around 20 to 60 drums. When marching, the players walk forward with short steps synchronized with the beat, which is important for embodying the rhythm, even though it is not audible.</p> <p>===============<br>Acknowledgments<br>===============</p> <p>This work was partially supported by the funding agency Comisión Sectorial de Investigación Científica, Universidad de la República, Uruguay, and by the Department of Culture of the Municipality of Montevideo.</p> <p>This is the complete list of performers, in alphabetical order: Mariano Barroso, Eduardo 'Cacho' Giménez, Eduardo 'Malumba' Giménez, Francisco Giménez, José Luis Giménez, Jorge 'Foqué' Gómez, José Pedro 'Perico' Gularte, Luis 'Pocholo' Maciel, Julio Magariños, Raúl 'Neno' Magariños, Marcelo Magariños, Javier 'Cerdo' Martirena, Wilson Martirena, Eduardo 'Tierra' Nilo, Sergio Ortuño, Fernando 'Lobo' Núñez, Edinson 'Palo' Oviedo, Gustavo Oviedo, Egdardo Pintos, Luis 'Mocambo' Quiroz, Rodolfo 'Pelado' Rodríguez, Fernando 'Hurón' Silva, Juan Silva, Raúl Silva, Waldemar 'Cachila' Silva, and Héctor Manuel Suárez.</p>
Fig. 1 in A Description Of The Male Drumming Call Of Besdolus Ventralis (Pictet, 1841) (Plecoptera: Perlodidae)
Fig. 1. Oscillograms at different time resolutions showing the amplitude modulation pattern of a Besdolus ventralis male call. A, an entire call; B, the first beat group of the call shown in A; C, the second beat group of the call shown in A; D, the third beat group of the call shown in A, red /\ symbols point toward individual beats. (Amplitude is normalised
Figure 1 in Blood cells and some hematological parameters of red drum (Linnaeus, 1766) in Vietnam
Figure 1. Erythrocytes of red drum. a - location L1, b - location L2, c - location L3. The red arrow indicates the deformed erythrocyte nuclear/uneven nuclear-matter distribution. The blue arrow indicates the slightly alkaline erythrocytes.
IDMT-SMT-Drums Dataset
<p>The IDMT-SMT-Drums database is a medium-sized database for automatic drum transcription and source separation.</p> <p>The dataset consists of 608 WAV files (44.1 kHz, Mono, 16bit). The approximate duration is 2:10 hours.</p> <p>There are 104 polyphonic drum set recordings (drum loops) containing only the drum instruments kick drum, snare drum and hi-hat. For each drum loop, there are 3 training files for the involved instruments, yielding 312 training files for drum transcription purposes. The recordings are from three different sources:</p> <ul> <li>Real-world, acoustic drum sets (RealDrum)</li> <li>Drum sample libraries (WaveDrum)</li> <li>Drum synthesizers (TechnoDrum)</li> </ul> <p>For each drum loop, the onsets of kick drum, snare drum and hi-hat have been manually annotated. They are provided as XML and SVL files that can be assigned to the corresponding audio recording by their filename. Appropriate annotation file parsers are provided as MATLAB functions together with an example script showing how to import the complete dataset.</p> <p>The subsets TechnoDrum02 and WaveDrum02 contain 64 drum loops that are delivered together with perfectly isolated single tracks of kick drum, snare drum, and hi-hat in addition to the above-mentioned training files. Mixing the single tracks together yields the mixture drum loops, thus providing 192 reference signals for source separation experiments.</p>
Dataset of the temperature rise during granular flows in a rotating drum
<p>This dataset contains the temperature rise of granular flows reported in the journal article "<em>Experimental investigation of heat generation during granular flow in a rotating drum using infrared thermography</em>" (<a href="https://doi.org/10.1016/j.powtec.2023.118619">https://doi.org/10.1016/j.powtec.2023.118619</a>).</p> <p>The dataset is separated into 4 files, one for each particle material: <em>plastic_particles.xlsx</em>, <em>plastic_particles.xlsx</em>, <em>plastic_particles.xlsx</em>, <em>plastic_particles.xlsx</em>.</p> <p>Each file has 3 tabs, one for the experiments with each number of particles: 400, 600, 800.</p> <p>Each tab has 3 tables, one for the experiments with each rotation speed: 15 rpm, 35 rpm, 55 rpm.</p> <p>Each table contains the temperature rise obtained in 5 experiments, along with its mean and standard deviation.</p>
Data from: Dynamics of diet-egg transfer of fatty acids in the teleost fish, red drum (Sciaenops ocellatus)
Open the record for dataset details and reuse information.
Stone-assisted drumming in Western chimpanzees and its implications for communication and cultural transmission
Open the record for dataset details and reuse information.
Input files and simulation results for reactive two-phase transport calculations in a waste package with OGS6-MP-LT-Drum
<p>The folders contain the original input and output files for reactive two-phase flow calculations with OpenGeoSys6-MP-LT-drum (doi:10.5281/zenodo.4060885), as well as the GEM-Selektor V3.3 project for look-up table generation, state<br> files for Paraview, R-script and jupyter notebook files used for post-processing the simulation output, which were used for creating the results in:</p> <p>Huang, Y., Shao, H., Wieland, E. <em>et al.</em> Two-phase transport in a cemented waste package considering spatio-temporal evolution of chemical conditions. <em>npj Mater Degrad</em> <strong>5, </strong>4 (2021). https://doi.org/10.1038/s41529-021-00150-z</p>
1000 Synthesized Drum Loops
<p>1000 drum loops generated using the synth stack search project</p> <p>https://github.com/imilas/Synths_Stacks_Search</p>
1st Empire Drum
This is a Submission for an assigment due for 23/11/2021 The guidelines were to recreate a props that we wanted. This drum is very Highly inspired by French Drum from the 1st Empire era but not stick at a historic reality ! A video presenting the object is available here: https://youtu.be/bIy7Si5w_8k Source: Objaverse 1.0 / Sketchfab
Djembe Goplet Drum - Photogrammetry Scan
Photogrammetry model of an african Djembe drum. The model was made with 3DF Zephyr and Blender, from 372 photos. Source: Objaverse 1.0 / Sketchfab
Drum on a caryatid
ID no.: MOK – M.O./A/404 Museum: African Museum in Olkusz https://muzea.malopolska.pl/en/objects-list/1897 Digitalisation: RDW MIC, Virtual Małopolska project Source: Objaverse 1.0 / Sketchfab
Garamut (Slit-drum)
Photogrammetry of a Garamut from Vanuatu - Melanesia MUSICAL INSTRUMENTS MUSEUM of Brussels Source: Objaverse 1.0 / Sketchfab
Drum Bongo 3D Scan Clean Low Poly
Drum Bongo 3D Scanned and cleaned up with Blender. -Low Poly -Optimized -4k Texture Source: Objaverse 1.0 / Sketchfab
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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.