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18 results for “midi”
Expanded Groove MIDI Dataset
<p>The Expanded Groove MIDI Dataset (E-GMD), a large dataset of human drum performances, with audio recordings annotated in MIDI. E-GMD contains 444 hours of audio from 43 drum kits and is an order of magnitude larger than similar datasets. It is also the first human-performed drum dataset with annotations of velocity.</p> <p>Additional information is available on the Magenta website: <a href="https://g.co/magenta/e-gmd">The Expanded Groove MIDI Dataset</a></p> <p>If you use the E-GMD dataset in your work, please cite the paper where it was introduced:</p> <p>Lee Callender, Curtis Hawthorne, and Jesse Engel. "<a href="https://goo.gl/magenta/e-gmd-paper">Improving Perceptual Quality of Drum Transcription with the Expanded Groove MIDI Dataset.</a>" 2020. arXiv:2004.00188.</p> <p>You can also use the following BibTeX entry:</p> <pre>@misc{callender2020improving, title={Improving Perceptual Quality of Drum Transcription with the Expanded Groove MIDI Dataset}, author={Lee Callender and Curtis Hawthorne and Jesse Engel}, year={2020}, eprint={2004.00188}, archivePrefix={arXiv}, primaryClass={cs.SD} } </pre> <p>Please also make sure to specify which version of the dataset you are using.</p>
List.MID: A MIDI-Based Benchmark for Evaluating RDF Lists
<p>Linked lists represent a countable number of ordered values, and are among the most important abstract data types in computer science. With the advent of RDF as a highly expressive knowledge representation language for the Web, various implementations for RDF lists have been proposed. Yet, there is no benchmark so far dedicated to evaluate the performance of triple stores and SPARQL query engines on dealing with ordered linked data. Moreover, essential tasks for evaluating RDF lists, like generating datasets containing RDF lists of various sizes, or generating the same RDF list using different modelling choices, are cumbersome and unprincipled. In this paper, we propose List.MID, a systematic benchmark for evaluating systems serving RDF lists. List.MID consists of a dataset generator, which creates RDF list data in various models and of different sizes; and a set of SPARQL queries. The RDF list data is coherently generated from a large, community-curated base collection of Web MIDI files, rich in lists of musical events of arbitrary length. We describe the List.MID benchmark, and discuss its impact and adoption, reusability, design, and availability.</p>
Violin MIDI Dataset
<h3>Violin MIDI Transcription Dataset from <strong><em><a title="High-Resolution Violin Transcription using Weak Labels" href="https://repositori.upf.edu/handle/10230/58121">High-Resolution Violin Transcription using Weak Labels</a></em></strong></h3> <h3><strong>Description</strong></h3> <p><strong>Overview</strong></p> <p>A descriptive transcription of a violin performance requires detecting not only the notes but also the fine-grained pitch variations, such as vibrato. Most existing deep learning methods for music transcription do not capture these variations and often need frame-level annotations, which are scarce for the violin. To enable the development of transcription methods tailored for the analysis of violin performances, this dataset includes MIDI files aligned to violin performances with 5.8 ms frame resolution and 10-cent frequency resolution, and includes pitch bends that represent fine-grained deviations such as vibrato and intonation choice.</p> <p><strong>Dataset Creation</strong></p> <p>The dataset consists of performances of three violin etude books by 22 violinists. The etude books included are:</p> <ul> <li>Paganini, Op. 1</li> <li>Wohlfahrt, Op. 35</li> <li>Kayser, Op. 20</li> </ul> <p>The original MIDI files pre-alignment are sourced from IMSLP and MuseScore (published under Creative Commons license). In the dataset, we provide these open-source MIDI files after they are aligned with the performance links from YouTube. Notably, each MIDI file includes pitch bends to offer a more realistic representation of the expressive performances, and to facilitate intonation analysis for pedagogical applications. The filenames are structured to include reconstructable links to the performances:</p> <p><code>{composer}_{catalog_number}_{performer}_{YouTube_ID}-{YouTube_start_sec}-{YouTube_end_sec}.mid</code></p> <p><strong>Contributions and Findings</strong></p> <p>We target the task of descriptive violin transcription with pitch bends, and demonstrate that our method (1) outperforms generic systems in the proxy tasks of violin transcription and pitch estimation, and (2) can automatically generate new training labels by aligning its feature representations with unseen scores. Additionally, we share our model along with 34 hours of score-aligned solo violin performance dataset, notably including the 24 Paganini Caprices.</p> <h3><strong>Citation</strong></h3> <p>If you find this dataset useful, please cite the following paper:</p> <blockquote> <p>Nazif Can Tamer, Yigitcan Özer, Meinard Müller, Xavier Serra, “High-Resolution Violin Transcription using Weak Labels”, in Proc. of the 24th Int. Society for Music Information Retrieval Conf., Milan, Italy, 2023.</p> </blockquote> <p><code>@inproceedings{tamer2023high,</code><br><code> title={High-Resolution Violin Transcription Using Weak Labels},</code><br><code> author={Tamer, Nazif Can and {\"O}zer, Yigitcan and M{\"u}ller, Meinard and Serra, Xavier},</code><br><code> booktitle={International Society for Music Information Retrieval Conference (ISMIR)},</code><br><code> year={2023}</code><br><code>}</code></p> <p> </p>
Saarland Music Data: MIDI-Audio Piano Music
<p>This is an improved version of the dataset originally referred to as <strong>SMD MIDI-Audio Piano Music.</strong> For more details, please visit the website: <a href="https://www.audiolabs-erlangen.de/resources/MIR/SMD/midi">https://www.audiolabs-erlangen.de/resources/MIR/SMD/midi</a></p> <p>Saarland Music Data provides audio recordings along with perfectly synchronized MIDI files for various piano pieces. The pieces were performed by students of the <a href="http://www.hfm.saarland.de">Hochschule für Musik Saar</a> on a hybrid acoustic/digital piano <a href="http://www.yamaha.com/Products/Disklavier.html">Yamaha Disklavier</a>. The Disklavier allows for capturing key and pedal movements of the piano while playing. This information, which can be stored in a MIDI file, yields an accurate annotation of the corresponding audio recording in form of a symbolic description of all played musical note events. The SMD MIDI-Audio pairs constitute a valuable dataset for various music analysis tasks such as music transcription, performance analysis, music synchronization, audio alignment, or source separation. </p> <p>All performances were recorded in the studios of the <a href="http://www.hfm.saarland.de">Hochschule für Musik Saar</a>, played by students of piano classes of different levels, on a <a href="http://www.yamaha.com/Products/Disklavier.html">Yamaha Disklavier</a> model <a href="http://www.yamaha.com/yamahavgn/CDA/ContentDetail/ModelSeriesDetail.html?CNTID=556850&CNTYP=PRODUCT">DCFIIISM4PRO</a>. Using two cardioid-condenser microphones fixed over the resonating body of the piano, all performances were directly recorded into Steinberg Cubase 4. Except for trimming the beginnings and ends of the recordings, no further post-processing (filters, effects) was applied to the musical material. From each Cubase project, an audio file (44.1 kHz, stereo) as well as a synchronized standard MIDI file (SMF) were exported. Besides these files, we also provide the audio files as WAV (22.05 kHz, mono) and the MIDI files encoded as CSV files and as WAV files (22.05 kHz, mono) rendered using the Software synthesizer <a href="https://www.fluidsynth.org/">FluidSynth</a>.</p> <p>SMD MIDI-Audio Piano Music (V1) contains the following data:</p> <ul> <li>wav_44100_stereo: Audio file (44.1 kHz, stereo)</li> <li>wav_22050_mono: Audio file (22.05 kHz, mono)</li> <li>midi: MIDI file</li> <li>csv: Export of note events from MIDI file into CSV format</li> <li>midi_wav_22050_mono: MIDI file rendered as audio file (22.05 kHz, mono)</li> </ul> <p>If you publish results obtained using this dataset, please cite:</p> <p>Meinard Müller, Verena Konz, Wolfgang Bogler, Vlora Arifi-Müller: Saarland Music Data (SMD). In Late-Breaking and Demo Session of the 12th International Conference on Music Information Retrieval (ISMIR), 2011. [<a href="https://www.audiolabs-erlangen.de/resources/MIR/SMD/2011_MuellerKonzBoglerArifi_SaarlandMusicData_ISMIR-LateBreaking.pdf">pdf</a>] [<a href="https://www.audiolabs-erlangen.de/resources/MIR/SMD/bibtex.html">bib</a>]</p>
The MIDI Linked Data Cloud dataset
<p>The study of music is highly interdisciplinary, and thus requires the combination of datasets from multiple musical domains, such as catalog metadata (authors, song titles, dates), industrial records (labels, producers, sales), and music notation (scores). While today an abundance of music metadata exists on the Linked Open Data cloud, datasets containing interoperable symbolic descriptions of music itself, i.e. music notation with note and instrument level information, are scarce. This is the MIDI Linked Data Cloud, a dataset that represents multiple collections of digital music in the MIDI standard format as Linked Data. At the time of writing, the dataset comprises 10,215,557,355 triples of 308,443 interconnected MIDI scores, and provides Web-compatible descriptions of their MIDI events.</p>
Fig. 1. A. A in Comparison of exposure ages and spectral properties of rock surfaces in steep, high alpine rock walls of Aiguille du Midi, France
Fig. 1. A. A slab from Maysville, KY (CMC IP50706) with a drilled specimen of the stylophoran mitrate Enoploura popei Caster, 1952. The hole indicated by an arrow. B. Close up of the drilled specimen. C. Detail of the borehole.
Fig. 2 in Comparison of exposure ages and spectral properties of rock surfaces in steep, high alpine rock walls of Aiguille du Midi, France
Fig. 2. Horizontal boring on the slab from Maysville, KY (CMC IP50706). A. Overlapping burrows in the shell of the brachiopod Rafinesquina. B. Burrows in a bryozoan. Scale bars 10 mm.
PiJAMA: Piano Jazz with Automatic MIDI Annotations
<p>Release of the automatic MIDI transcriptions that constitute the PiJAMA dataset. See below for the abstract of the publication.<br> <br> Abstract:</p> <p>Recent advances in automatic piano transcription have enabled large scale analysis of piano music in the symbolic domain. However, the research has largely focused on classical piano music. We present <strong>PiJAMA</strong> (<strong>Pi</strong>ano <strong>J</strong>azz with <strong>A</strong>utomatic <strong>M</strong>IDI <strong>A</strong>nnotations): a dataset of over 200 hours of solo jazz piano performances with automatically transcribed MIDI. In total there are 2,777 unique performances by 120 different pianists across 244 recorded albums. The dataset contains a mixture of studio recordings and live performances. We use automatic audio tagging to identify applause, spoken introductions, and other non-piano audio to facilitate downstream music information retrieval tasks. We explore descriptive statistics of the MIDI data, including pitch histograms and chromaticism. We then demonstrate two experimental benchmarks on the data: performer identification and generative modeling. The dataset, including a link to the associated source code is available at <a href="https://almostimplemented.github.io/PiJAMA/">https://almostimplemented.github.io/PiJAMA/</a>.</p>
Groove2Groove MIDI Dataset: synthetic accompaniments in 3k styles
<p>The <em>Groove2Groove MIDI Dataset</em> is a parallel corpus of synthetic MIDI accompaniments in almost 3000 different styles, created as described in the paper <em><a href="https://doi.org/10.1109/TASLP.2020.3019642">Groove2Groove: One-Shot Accompaniment Style Transfer with Supervision from Synthetic Data</a></em> [<a href="https://groove2groove.telecom-paris.fr/data/paper.pdf">pdf</a>]. See the <code>README.md</code> file or the <em><a href="https://groove2groove.telecom-paris.fr/#Dataset">Groove2Groove website</a></em> for more information.</p> <p>The dataset is split into the following sections:</p> <ul> <li><code>train</code> contains 5744 MIDI files in 2872 styles (exactly 2 files per style). Each file contains 252 measures following a 2 measure count-in.</li> <li><code>val</code> and <code>test</code> each contain 1200 files in 40 styles (exactly 30 files per style, 16 bars per file after the count-in). The sets of styles are disjoint from each other and from those in <code>train</code>.</li> <li><code>itest</code> is generated from the same chord charts as <code>test</code>, but in 40 styles from the training set.</li> </ul> <p>Chord charts for all MIDI files are provided in the ABC format and the Band-in-a-Box (MGU) format. Each chord chart corresponds to at least 2 MIDI files in different styles.</p> <p>The code used to automate Band-in-a-Box is available in the <a href="https://github.com/cifkao/pybiab">pybiab</a> package.</p> <p>If you use the data in your research, please reference the paper (not just the Zenodo record):</p> <pre><code>@article{groove2groove, author={Ond\v{r}ej C\'{i}fka and Umut \c{S}im\c{s}ekli and Ga\"{e}l Richard}, title={{Groove2Groove}: One-Shot Music Style Transfer with Supervision from Synthetic Data}, journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing}, publisher={IEEE}, year={2020}, volume={28}, pages={2638--2650}, doi={10.1109/TASLP.2020.3019642}, url={https://doi.org/10.1109/TASLP.2020.3019642} }</code></pre>
Petite pause à l'ombre après une après-midi de «chasse» entomologique. in Société Vaudoise D'Entomologie (Sve)
Petite pause à l'ombre après une après-midi de «chasse» entomologique.
Safety and Performance of Miami InnFocus Drainage Implant (MIDI Arrow) Glaucoma Drainage Implant
ClinicalTrials.gov study NCT01563237. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Composer Attribution of Renaissance Motets (Iberian Polyphony around 1500): MIDIs and Extracted Features
<p><br> This distribution includes MIDI files used in the experiments described in the "Composer Attribution of Renaissance Motets: A Case Study Using Statistical Features and Machine Learning" chapter of the book The Anatomy of Iberian Polyphony around 1500. Multi-part motets have been separated out into separate MIDI files. We edited the files for consistency, which included adjusting elements that could interfere with encoding rhythm consistently (e.g. standardized rhythmic value rates, eliminating fermatas, etc.).</p> <p>The MIDI files in the nonIb_noMB group were taken from the Josquin Research Project (JRP), who have kindly granted us permission to re-publish them here with our modifications. These are redistributed with a "CC BY-SA 4.0" license: https://github.com/josquin-research-project/jrp-scores/blob/master/LICENSE.txt. The Iberian MIDI files were produced from editions created by The Anatomy of Late 15th- and Early 16th-Century Iberian Polyphonic Music project (https://iberianpolyphonicmusic.wordpress.com), and are included here after our modifications, with permission. We digitised the nonIb_MBonly MIDI files ourselves using Sibelius. All these files are distributed under a "CC BY-SA 4.0" license" license (https://creativecommons.org/licenses/by-sa/4.0/). The included "Catalogue.pdf" file outlines the contents of this corpus in its entirety.</p> <p>This distribution also includes the "Renaissance-safe" features extracted using jSymbolic 2.2 (http://jmir.sourceforge.net) from the MIDI encodings included here. Details on all the features extracted with the software are available in the jSymbolic manual (http://jmir.sourceforge.net/manuals/jSymbolic_manual/home.html). These are presented as follows:</p> <p>- FeatureDefinitions.xml: Descriptions of all extracted features, encoded in ACE XML 1.0, as output directly by jSymbolic. This file does not include any feature values (these are found in the FeatureValues.xml file).</p> <p>- FeatureValues.xml: Extracted feature values, encoded in ACE XML 1.0, as output directly by jSymbolic. The features are described in the FeatureDefinitions.xml file. Class values are implied by the folder containing each MIDI file.</p> <p>- FeatureValues_BasicCSV: Extracted feature values encoded in a CSV file, as output directly by jSymbolic (with complete file paths truncated). Class values are implied by the folder containing each MIDI file.</p> <p>- FeatureValues_HumanReadable.xlsx: Extracted features formatted into a human-readable Microsoft Excel file. Group averages and standard deviations have been added to the bottom, and the "Length_In_Breves" feature is added in a column at the right (it is included separately because it is not calculated directly by jSymbolic 2.2).</p> <p>- FeatureValues_WekaReady.csv: Extracted feature values encoded in a CSV file in a format readable by Weka (https://www.cs.waikato.ac.nz/ml/weka/). Class values have been added in a column on the right, and file paths have been removed, as required by Weka.</p>
Ejemplo de creación musical desarrollado por los autores en BandLab con instrumentos MIDI y cajas de ritmos
Open the record for dataset details and reuse information.
Material for the paper "MIDI2vec: Learning MIDI Embeddings for Reliable Prediction of Symbolic Music Metadata"
<p>Edgelists and trained embeddings used in the paper</p>
MIDI (MR Imaging Abnormality Deep Learning Identification)
ClinicalTrials.gov study NCT04368481. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Transcriptome profiling of mutant INS gene-induced diabetes of youth (MIDY) and wild type pig livers.
GEO Series GSE122029. Sus scrofa. 9 samples. Type: Expression profiling by high throughput sequencing.
Los Angeles MIDI Dataset
<p>SOTA kilo-scale MIDI dataset for MIR and Music AI purposes</p> <p> </p> <p>Main Features:</p> <p>1) ~600000 MIDIs to explore :)</p> <p>2) Each MIDI file was read-checked and 100% de-duped</p> <p>3) Extensive meta-data for each MIDI file</p> <p>4) Detailed features matrix for each MIDI file</p> <p>5) Helper Python code</p> <p> </p> <p>Project Los Angeles</p> <p>Tegridy Code 2023</p>
UMD-350MB: Refined MIDI Dataset for Symbolic Music Generation
<p><strong>UMD-350MB</strong></p> <p>The Universal MIDI Dataset 350MB (UMD-350MB) is a proprietary collection of 85,618 MIDI files curated for research and development within our organization. This collection is a subset sampled from a larger dataset developed for pretraining symbolic music models.</p> <p>The field of symbolic music generation is constrained by limited data compared to language models. Publicly available datasets, such as the Lakh MIDI Dataset, offer large collections of MIDI files sourced from the web. While the sheer volume of musical data might appear beneficial, the actual amount of valuable data is less than anticipated, as many songs contain less desirable melodies with erratic and repetitive events.</p> <p>The UMD-350MB employs an attention-based approach to achieve more desirable output generations by focusing on human-reviewed training examples of single-track melodies, chord progressions, leads and arpeggios with an average duration of 8 bars. This was achieved by refining the dataset over 24 months, ensuring consistent quality and tempo alignment. Moreover, the dataset is normalized by setting the timing information to 120 BPM with a tick resolution (PPQ) of 96 and transposing the musical scales to C major and A minor (natural scales).</p> <p><strong>Melody Styles</strong></p> <p>A major portion of the dataset is composed of newly produced private data to represent modern musical styles.</p> <ul> <li>Pop: 1970s to 2020s Pop music</li> <li>EDM: Trance, House, Synthwave, Dance, Arcade</li> <li>Jazz: Bebop, Ballad, Latin-Jazz, Bossa-Jazz, Ragtime</li> <li>Soul: 80s Classic, Neo-Soul, Latin-Soul</li> <li>Urban: Pop, Hip-Hop, Trap, R&B, Afrobeat</li> <li>World: Latin, Bossa Nova, European</li> <li>Other: Film, Cinematic, Game music and piano references</li> </ul> <p><em>Actual MIDI files are unlabeled for unsupervised training.</em></p> <p><strong>Dataset Access</strong></p> <p>Please note that this is a closed-source dataset with very limited access. Considerations for access include proposals for data augmentation, chord extraction and other enhancement methods, whether through scripts, algorithmic techniques, manual editing in a DAW or additional processing methods.</p> <p>For inquiries about this dataset, please email us.</p>
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