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16 results for “chords”
IDMT-SMT-Chord-Sequences Dataset
<p>The IDMT-SMT-CHORD-SEQUENCES dataset contains 15,000 4-32 seconds long chord progressions synthesized from MIDI data with 45 different instruments. The files are organized as 5,000 triplets with an anchor, similar and dissimilar chord progression. Metadata like tempo, meter, duration in bars, instrument, and chord progression is provided in JSON format. Also, an overview about all metadata along with the dataset split is stored in a CSV file.</p>
Simple Chords Dataset
<p>This dataset contains 3 subdatasets:</p> <p>1. Triad Dataset</p> <p> This dataset contains 1.080 piano-roll examples of triads (3-note chords). Each example is a 45-d vector where each vector entry represents a single note and its value represents the volume of that note between 0 and 1. Table 1 shows the connection between a musical note and its corresponding entry on the piano-roll vector.</p> <table> <caption>Table 1: Piano-roll vector to note</caption> <thead> <tr> <th scope="col">Vector Entry</th> <th scope="col">Note</th> <th scope="col">Vector Entry</th> <th scope="col">Note</th> <th scope="col">Vector Entry</th> <th scope="col">Note</th> <th scope="col">Vector Entry</th> <th scope="col">Note</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>Cn1</td> <td>13</td> <td> <p>Cn2</p> </td> <td>25</td> <td>Cn3</td> <td>37</td> <td>Cn4</td> </tr> <tr> <td>2</td> <td>Db1</td> <td>14</td> <td>Db2</td> <td>26</td> <td>Db3</td> <td>38</td> <td>Db4</td> </tr> <tr> <td>3</td> <td>Dn1</td> <td>15</td> <td>Dn2</td> <td>27</td> <td>Dn3</td> <td>39</td> <td>Dn4</td> </tr> <tr> <td>4</td> <td>Eb1</td> <td>16</td> <td>Eb2</td> <td>28</td> <td>Eb3</td> <td>40</td> <td>Eb4</td> </tr> <tr> <td>5</td> <td>En1</td> <td>17</td> <td>En2</td> <td>29</td> <td>En3</td> <td>41</td> <td>En4</td> </tr> <tr> <td>6</td> <td>Fn1</td> <td>18</td> <td>Fn2</td> <td>30</td> <td>Fn3</td> <td>42</td> <td>Fn4</td> </tr> <tr> <td>7</td> <td>Gb1</td> <td>19</td> <td>Gb2</td> <td>31</td> <td>Gb3</td> <td>43</td> <td>Gb4</td> </tr> <tr> <td>8</td> <td>Gn1</td> <td>20</td> <td>Gn2</td> <td>32</td> <td>Gn3</td> <td>44</td> <td>Gn4</td> </tr> <tr> <td>9</td> <td>Ab1</td> <td>21</td> <td>Ab2</td> <td>33</td> <td>Ab3</td> <td>45</td> <td>Ab4</td> </tr> <tr> <td>10</td> <td>An1</td> <td>22</td> <td>An2</td> <td>34</td> <td>An3</td> <td> </td> <td> </td> </tr> <tr> <td>11</td> <td>Bb1</td> <td>23</td> <td>Bb2</td> <td>35</td> <td>Bb3</td> <td> </td> <td> </td> </tr> <tr> <td>12</td> <td>Bn1</td> <td>24</td> <td>Bn2</td> <td>36</td> <td>Bn3</td> <td> </td> <td> </td> </tr> </tbody> </table> <p>Each example is a basic triad. There are 10 triad types in this dataset. Each triad musical structure and vector structure are shown on table 2. The vector structure shows the second and third note position relative to the first note of the triad. Augmented on first inversion and Augmented on second inversion have the same vector structure as Augmented so they are indistinguishable from one another, therefore, they are not added to the dataset to avoid repetition of examples.</p> <p> </p> <table> <caption>Table 2: Musical and vector structure of dataset triads</caption> <thead> <tr> <th scope="col">Triad Name</th> <th scope="col">Musical Structure</th> <th scope="col">Vector Structure</th> </tr> </thead> <tbody> <tr> <td>Major (m)</td> <td>(first, major third, fifth)</td> <td>(0,4,7) </td> </tr> <tr> <td>Major first inversion (m1)</td> <td>(major third, fifth, eight)</td> <td>(0,3,8) </td> </tr> <tr> <td>Major second inversion (m2)</td> <td>(fifth, eight, major tenth)</td> <td>(0,5,9) </td> </tr> <tr> <td>Minor (n)</td> <td>(first, minor third, fifth)</td> <td>(0,3,7) </td> </tr> <tr> <td>Minor first inversion (n1)</td> <td>(minor third, fifth, eight)</td> <td>(0,4,9) </td> </tr> <tr> <td>Minor second inversion (n2)</td> <td>(fifth, eight, minor tenth)</td> <td>(0,5,8) </td> </tr> <tr> <td>Diminished (d) </td> <td>(first, minor third, diminished fifth)</td> <td>(0,3,6) </td> </tr> <tr> <td>Diminished first inversion (d1)</td> <td>(minor third, diminished fifth, eight)</td> <td>(0,3,9) </td> </tr> <tr> <td>Diminished second inversion (d2)</td> <td>(diminished fifth, eight, minor tenth)</td> <td>(0,6,7) </td> </tr> <tr> <td>Augmented (a)</td> <td>(first, major third, augmented fifth)</td> <td>(0,4,8) </td> </tr> </tbody> </table> <p> </p> <p>There are examples of all the triad types with all the 12 available notes of the western musical system as base note: Cn, Db, Dn, Eb, En, Fn, Gb, Gn, Ab, An, Bb, and Bn. (n is natural, b is flat). Also, the chords were recorded across 3 octaves for a total of 36 base notes. The vector has 45 entries because when the base note is Bb3 (36th entry), on the worst case (major triad second inversion or diminished triad first inversion), the third note is an Ab4 (45th entry). Finally, each example was created with three different volumes: <em>forte</em> (f) with an amplitude of 1, <em>metsoforte</em> (m) with an amplitude of 2/3, and <em>piano</em> (p) with an amplitude of 1/3.</p> <p>On resume, there are 10 triad types, 12 base notes, 3 octaves and 3 volumes for a total of 10*12*3*3 = 1,080 examples. The dataset is saved on a Numpy array.</p> <p> </p> <p>2. Tetrad Dataset</p> <p>The Tetrad Dataset contains 792 examples of tetrads (4-note chords). Just like the Triad Dataset, each example is a 45-d vector where each vector entry represents a single note and it value represents the volume of that note between 0 and 1.</p> <p>Each example is a basic seventh-chord tetrad. There are 8 seventh chord types in this dataset. Each tetrad musical structure and vector structure are shown on table 3. The vector structure shows the second, third and fourth note position relative to the first note of the tetrad.</p> <p> </p> <table> <caption>Table 3: Musical and vector structure of dataset tetrads</caption> <tbody> <tr> <td>Tetrad Name</td> <td>Musical Structure</td> <td>Vector Structure</td> </tr> <tr> <td>Augmented Major (augMaj7)</td> <td>(first, major third, augmented fifth, major seventh)</td> <td>(0,4,8, 11)</td> </tr> <tr> <td>Major Seventh (maj7) </td> <td>(first, major third, fifth, major seventh)</td> <td>(0,4,7, 11)</td> </tr> <tr> <td>Minor Major Seventh (minMaj7)</td> <td>(first, minor third, augmented fifth, major seventh)</td> <td>(0,3,7, 11)</td> </tr> <tr> <td>Augmented Seventh (aug7)</td> <td>(first, major third, augmented fifth, minor seventh)</td> <td>(0,4,8, 10)</td> </tr> <tr> <td>Dominant Seventh (7)</td> <td>(first, major third, fifth, minor seventh)</td> <td>(0,4,7, 10)</td> </tr> <tr> <td>Minor Seventh (m7)</td> <td>(first, minor third, fifth, minor seventh)</td> <td>(0,3,7, 10)</td> </tr> <tr> <td>Half Diminished (m7b5)</td> <td>(first, minor third, diminished fifth, minor seventh)</td> <td>(0,3,6, 10)</td> </tr> <tr> <td>Diminished Seventh (dim7)</td> <td>(first, minor third, diminished fifth, diminished seventh)</td> <td>(0,3,6, 9)</td> </tr> </tbody> </table> <p>Each of this tetrads were recorded with 33 base notes, ranging from Cn1 to An3 as shown in \ref{tab:vector_to_note} to make sure t0 use all the 45 notes available and avoiding overflow that will lead to less than 4 notes per chord. Finally, each example was created with three different volumes: <em>forte</em> (f) with an amplitude of 1, <em>metsoforte</em> (m) with an amplitude of 2/3, and <em>piano</em> (p) with an amplitude of 1/3.</p> <p>On resume, there are 8 tetrad types, 33 base notes and 3 volumes for a total of 8*33*3 = 792 examples. The dataset is saved on a Numpy array.</p> <p> </p> <p> </p> <p>3. Random Dataset</p> <p> </p> <p>The Random Validation Dataset is a dataset that contains 3,000 examples 3 random notes distributed uniformly across the 45 possible different notes. 1,000 examples are generated by selecting 3 random notes out of the 45 possible notes. Then, each of the 1,000 examples is saved on the three possible different volumes: <em>forte</em> (f) with an amplitude of 1, <em>metsoforte</em> (m) with an amplitude of 2/3, and <em>piano</em> (p) with an amplitude of 1/3. This gives a total of 3,000 examples.</p>
Dataset: Chord Energy Corporation (CHRD) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
IDMT-SMT-Chords Dataset
<p>The IDMT-SMT-CHORDS comprises of 16 MIDI generated audio files consists of various chord classes. Here we focused on chord voicings, which are commonly used on keyboard instruments and guitars. Based on this we categorized as Guitar and Non-Guitar instruments. We used several software instruments from Ableton Live and Garage Band to synthesize these MIDI files with various instruments such as piano, synthesizer pad, as well as acoustic and electric guitar.</p> <ul> <li>File duration: 4.1 Hours</li> <li># Chord segments: 7398</li> <li># WAV files: 16</li> <li>Chord duration: 2 seconds</li> <li>BPM: 120</li> <li>Time signature: 4/4</li> <li>Sampling rate: 44.1KHz</li> <li>Mono audio</li> </ul> <p>Non-Guitar</p> <p>The Non-Guitar files includes all chord types in all possible root note positions and inversions. For example, C Major triad chord is included with its two possible inversions C/E and C/G.</p> <p>All non-guitar chord classes are listed below:</p> <ul> <li>Major (+ 2 inversions)</li> <li>Minor (+ 2 inversions)</li> <li>Major 7 (+ 3 inversions)</li> <li>Minor 7 (+ 3 inversions)</li> <li>Power Chord - root and fifth note (+ 1 inversion)</li> <li>Dominant 7 (+ 3 inversions)</li> <li>Minor 7 flat 5 (+ 3 inversions)</li> </ul> <p>This gives us 576 non-guitar chord classes.</p> <p>Guitar</p> <p>The guitar files where generated based on barŕe chord voicings with the root note located on the low E, A, and D strings. For example, to modeling major chord and it’s voicings we use open position E maj, A maj and D maj shape and move 12 steps (including octave at 12<sup>th</sup> fret) thereby we get 39 positions (13*3).</p> <p>List of Guitar chord types:</p> <ul> <li>Major (+ 2 voicings)</li> <li>Minor (+ 2 voicings)</li> <li>Major 7 (+ 2 voicings)</li> <li>Minor 7 (+ 2 voicings)</li> <li>Power Chord - root and fifth note (+ 2 voicings)</li> <li>Dominant 7 (+ 2 voicings)</li> <li>Minor 7 flat 5 (+ 2 voicings)</li> </ul> <p>This gives us 273 different guitar chord classes.</p>
ChoCo: the Chord Corpus
<p><strong>Overview</strong></p> <p>We are happy to announce the new release of ChoCo, which has now reached <code>v1.0.0</code>. This ChoCo release includes updates and improvements to ChoCo's licensing and symbolic collections. We have adopted a dual licensing scheme for ChoCo's data, and regenerated all symbolic collections following improvements to the JAMifier, providing a more meaningful and complete version of JAMS files.</p> <p>📄 <strong>Licensing Updates</strong></p> <p>We have adopted a dual licensing scheme for ChoCo's data. Only three subsets are released using the <code>CC-BY-NC-SA 4.0</code> license, while all the others follow the <code>CC-BY 4.0</code> license. We include the <code>LICENSE.md</code> file in the release which contains all the details.</p> <p>🎼 <strong>Symbolic Collection Updates</strong></p> <p>All symbolic collections have been regenerated following improvements to the JAMifier. Measures and beats now always start from <code>1</code>, and time signatures are included in the JAMS files. Temporal information such as onset and duration is now expressed in measures and beats, making our JAMS files and KG more meaningful.</p> <p>❇️ <strong>Knowledge Graph Updates</strong></p> <p>The Knowledge Graph was regenerated to reflect the changes made to the JAMifier and the JAMS files produced.</p>
Data from: The Chord-Normalized Expected Species Shared (CNESS)-distance represents a superior measure of species turnover patterns
<p>1. Measures of β-diversity characterizing the difference in species composition between samples are commonly used in ecological studies. Nonetheless, commonly used dissimilarity measures require high sample completeness, or at least similar sample sizes between samples. In contrast, the Chord-Normalized Expected Species Shared (CNESS) dissimilarity measure calculates the probability of collecting the same set of species in random samples of a standardized size, and hence is not sensitive to completeness or size of compared samples. To date, this index has enjoyed limited use due to difficulties in its calculation and scarcity of studies systematically comparing it with other measures.</p> <p>2. Here, we developed a novel R function that enables users to calculate ESS (Expected Species Shared)-associated measures. We evaluate the performance of the CNESS index based on simulated datasets of known species distribution structure, and compared CNESS with more widespread dissimilarity measures (Bray-Curtis index, Chao-Sørensen index, and proportionality based Euclidean distances) for varying sample completeness and sample sizes.</p> <p>3. Simulation results indicated that for small sample size (m) values, CNESS chiefly reflects similarities in dominant species, while selecting large m values emphasizes differences in the overall species assemblages. Permutation tests revealed that CNESS has a consistently low CV (coefficient of variation) even where sample completeness varies, while the Chao-Sørensen index has a high CV particularly for low sampling completeness. CNESS distances are also more robust than other indices with regards to undersampling, particularly when chiefly rare species are shared between two assemblages.</p> <p>4. Our results emphasize the superiority of CNESS for comparisons of samples diverging in sample completeness and size, which is particularly important in studies of highly mobile and species-rich taxa where sample completeness is often low. Via changes in the sample size parameter m, CNESS furthermore cannot only provide insights into the similarity of the overall distribution structure of shared species, but also into the differences in dominant and rare species, hence allowing additional, valuable insights beyond the capability of more widespread measures.<br> </p>
Light Curves from a Tri-chord Stellar Occultation by Jupiter's Trojan Diomedes in 2020
<p>Light curves for the three occultation chords. </p> <p> </p> <p>bender_lc.dat : occultation light curve dataset from the data provided by observer K. Bender.</p> <p>kitting_lc.dat : occultation ligth curve dataset from the data provided by observer C. Kitting.</p> <p>oesper_lc.dat : occultation light curve dataset from the data provided by observer D. Oesper. </p> <p>README.txt : column label of all light curves dataset. </p> <p> </p>
Bach Chorales Multiple Chord Labels (BCMCL) dataset
<p>The Bach Chorales Multiple Chord Labels dataset was proposed in our DLfM (Digital Library for Musicology) 2020 conference paper: "Automatic Chord Labelling: A Figured Bass Approach". There are four parallel tracks of chord labels, produced by each of the four rule-based algorithms (Algorithms A, B, C, and D). The chord labels of each algorithm can be found in each folder, where the music (Bach chorales) and annotations are presented in the MusicXML format. Overall, there are 120 Bach chorales (or 123 since there are three chorales with two versions) annotated this way.</p> <p>If you make use of BCMCL, please cite the following <a href="https://dl.acm.org/doi/abs/10.1145/3424911.3425513">publication</a>:</p> <pre><code>@inproceedings{ju_automatic_2020, title = {Automatic {Chord} {Labelling}: {A} {Figured} {Bass} {Approach}}, shorttitle = {Automatic {Chord} {Labelling}}, booktitle = {Proceedings of the 7th {International} {Conference} on {Digital} {Libraries} for {Musicology}}, author = {Ju, Yaolong and Margot, Sylvain and McKay, Cory and Fujinaga, Ichiro}, year = {2020}, pages = {27--31}, } </code></pre> <p>Please email to <strong>yaolong.ju@mail.mcgill.ca</strong> if you have any questions.</p>
Data from: The Chord-Normalized Expected Species Shared (CNESS)-distance represents a superior measure of species turnover patterns
Open the record for dataset details and reuse information.
Chord voicings for 370 chorales by J. S. Bach as generated by the 'voicer' algorithm
<p>This is a dataset of 370 chorales by J S. Bach as generated by the 'voicer' algorithm for automatic chord voicing. This dataset is intended to accompany the paper 'A Computational Model for the Analysis and Generation of Chord Voicings'.</p> <p>Note: there are 370 chorales, but the numbering goes from 1 to 371, with one chorale omitted, matching the original dataset from http://kern.humdrum.org.</p>
Gene expression of human chord blood hematopoietic progenitor cells and induced hemato-endothelial progenitor cells derived from human iPSC
GEO Series GSE140221. Homo sapiens. 18 samples. Type: Expression profiling by high throughput sequencing.
Gene expression data of human chord blood hematopoietic progenitor cells subjected to myeloid differentiation and transduced with shRNAs against LINC00173
GEO Series GSE98829. Homo sapiens. 9 samples. Type: Expression profiling by array.
Music Therapy to Address Patients' Journeys With Chronic Illness, Outcomes, and Readmission - MAJOR CHORD RCT
ClinicalTrials.gov study NCT06546319. IPD Sharing: NO. Countries: 1. Publications: 0.
Infection of Myeloid Angiogenic Cells (MACs) with Bartonella henselae (B.h.) induces a chord formation phenotype in vitro.
GEO Series GSE55170. Homo sapiens. 7 samples. Type: Expression profiling by array.
Gene expression data of human chord blood hematopoietic progenior cells subjected to myeloid differentiation and transduced with lncRNA HOXA10-AS
GEO Series GSE123694. Homo sapiens. 6 samples. Type: Expression profiling by array.
MSK-CHORD
Targeted sequencing of 25,040 tumors from 24,950 patients and their matched normals via MSK-IMPACT, along with clinical annotations, some of which are derived from natural language processing (denoted NLP). This data is available under the Creative Commons BY-NC-ND 4.0 license. The cases here are only those under the age of 40 years old.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.