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5 results for “drums dataset”
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>
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>
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>
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