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112 results for “music dataset”
Greek Folk Music Dataset
<p>This work contains a dataset of Greek Aegean folk music tunes focusing on two prominent dances, syrtos and balos, and associated feature-pattern analysis.</p> <p><strong>About the Dataset</strong></p> <p>This dataset was developed to support research in computational musicology, providing access to Greek musical heritage through manually transcribed MIDI scores, aligned lyrics, and rich metadata.</p> <p><strong>Methodology</strong></p> <p>Through pattern analysis (using a tailored version of [PatMinr](https://github.com/olivierlar/miningsuite)) and feature extraction, we examine both shared melodic structures and unique characteristics of each dance, with some examples reflecting traces of oral transmission. While metadata accompanies the collection to support organization and context, our primary emphasis is on the musical and lyrical content.</p> <p><strong>Results</strong></p> <p>Our analysis revealed that balos and syrtos dances share strong tonal, rhythmic, and melodic similarities, often functioning as a performance pair, though they differ in tonal emphases, melodic tendencies, and lyrical themes. Both dances exhibit substantial shared melodic-rhythmic patterns, reflecting common musical vocabulary across the Aegean, though each retains distinct motif preferences and regional variations influenced by oral transmission.</p> <p><strong>Usage</strong></p> <p>Researchers and enthusiasts interested in Greek folk music can utilize this dataset to study and explore the intricate musical nuances prevalent within folk traditions.</p> <p><strong>Analysis </strong></p> <p>Note: You will need to use MATLAB with <a href="https://www.jyu.fi/hytk/fi/laitokset/mutku/en/research/materials/mirtoolbox">MIRToolbox</a> and <a href="https://github.com/miditoolbox/miditoolbox">MIDIToolbox</a>. <br>For code and full analysis pipelines, visit our <a href="https://github.com/MuTecEn/Multimodal-Greek-Folk-Music-Dataset">GitHub repository</a>.</p>
Emotion4MIDI: A Lyrics-Based Emotion-Labeled Symbolic Music Dataset
<p>This dataset includes emotion labels for the publicly available MIDI dataset, namely Lakh MIDI Dataset and Reddit MIDI dataset. The values represent the probability of containing a particular emotion. For a single song, more than one emotion can be present, hence the values don't add up to 1.</p>
Underlying REVISED dataset for the study: Singing and music making: Physiological responses across early to later stages of dementia
<p>These files contain the REVIDED underlying data for the study, Singing and Music Making: Physiological Responses Across Early to Later Stages of Dementia; physiological data from Study 1 and Study 2, and video recording engagement scores from Study 2.</p> <p><strong>in Study 2, the electrodermal readings (EDA) for the Slow Music readings in session 1 had become corrupted. The correct EDA data for this session was uploaded on 31.3.24.</strong></p> <p>The project was funded by the Wellcome Trust as part of The Hub Award at the Wellcome Collection, London. The article that these data are based on can be here: https://wellcomeopenresearch.org/articles/6-150/v1, Wellcome Open Research, 6:150.</p> <p>The abstract from the accompanying article:</p> <p><strong>Background</strong>: Music based interventions have been found to improve the wellbeing of people living with dementia. Research to date has primarily used psychometric questionnaires and qualitative interviews to determine impact and efficacy. More recently there has been an interest in exploring if psychophysiological measures could provide additional information about how music, singing and other arts activities impact this population. Physiological responses can provide additional evidence about an individual’s experience of an activity and may be particularly useful for people who are experiencing difficulties with communication. </p> <p><strong>Methods:</strong> This multiple-case study design drew on previously collected, unanalysed archival data and explored the physiological responses of nine people with mild-to-moderate dementia during a singing group, and six people in the later stages of dementia during an interactive music group. Medical grade Empatica E4 ™ wearable wristbands provided information on heart rate (HR), electrodermal activity (EDA), movement (ACC) and skin temperature (ST). The interactive music group was video recorded using non-intrusive Fly 360-degree cameras™ in order to provide additional group interactive information about engagement. </p> <p><strong>Results</strong>: Physiological responses were analysed using simulation modelling analysis (SMA) within individual case studies. Participants in the singing group showed an increase in EDA and HR as the session began. HR and ST increased during faster paced songs. EDA, movement and engagement were all higher during an interactive music group than during a control session (music listening). EDA and ST increased and in contrast to the responses during singing, HR decreased as the sessions began. EDA was higher during slower music, however this was less consistent in the more interactive intervention sessions than the control. There were no consistent changes in HR and movement responses during different styles of music. Physiological responses peaked during familiar music, interactions, physical touch in addition to times that participants appeared disengaged. </p> <p><strong>Conclusion</strong>: Non-intrusive physiological measures obtained from easily worn wristband devices may provide valuable information about the experiences of people living with dementia who participate in arts and other activities, particularly for those in later stages when verbal communication may be more difficult and it is no longer possible to complete psychometric questionnaires. However, whenever feasible, they should be used in conjunction with other measures to develop a more nuanced understanding of these experiences. Future research should consider using physiological measures with video-analysis and observational measures, where possible, to explore further how engagement in specific activities, wellbeing and physiology interact. This may provide valuable information for further development of activities and services for those living with dementias across different levels of impairment.</p>
Indian Art Music Tonic Datasets
<p><strong>Introduction</strong></p> <p>These datasets comprise audio excerpts and manually done annotations of the tonic pitch of the lead artist for each audio excerpt. Each excerpt is accompanied by its associated editorial metadata. These datasets can be used to develop and evaluate computational approaches for automatic tonic identification in Indian art music. These datasets have been used in several articles mentioned below. A majority of these datasets come from the <a href="http://compmusic.upf.edu/corpora">CompMusic corpora</a> of Indian art music, for which each recording is associated with a <a href="https://musicbrainz.org/doc/MusicBrainz_Identifier">MBID</a>. With the MBID other information can be obtained using the <a href="http://dunya.compmusic.upf.edu/developers/">Dunya API</a>. We here provide an overview of the tonic identification datasets. </p> <p><strong>Datasets </strong></p> <p>The statistics about the datasets for tonic identification is listed in the table below. These six datasets are used in [1] for a comparative evaluation. To the best of our knowledge these are the largest datasets available for tonic identification for Indian art music. These datases vary in terms of the audio quality, recording period (decade), the number of recordings for Carnatic, Hindustani, male and female singers and instrumental and vocal excerpts. For a detailed information about these datasets we refer to Chapter 3 of this <a href="http://mtg.upf.edu/node/3592">thesis</a>.</p> <p>All the datasets (annotations) are version controlled. To know how the features are extracted visit the companion page for the <a href="http://compmusic.upf.edu/node/323">publication</a>.</p> <p>The audio files corresponding to these datsets are made available on request for only research purposes. To obtain the files, please refer to <a href="https://zenodo.org/record/7342372">this Zenodo entry</a>.</p> <p><strong>Annotation Format </strong></p> <p>The tonic annotations are availabe both in tsv and json format. </p> <p>TSV: <relative path to audio><tab><tonic(Hz)><tab><Carnatic or Hindustani><tab><artist_name><tab><gender of the singer><vocal or instrumental> </p> <p>JSON: {<br> 'artist': <name of the lead artist if available>, </p> <p> 'filepath': <relative path to the audio file>,</p> <p> 'gender': <gender of the lead singer if available>,</p> <p> 'mbid': <musicbrainz id when available>,</p> <p> 'tonic': <tonic in Hz>,</p> <p> 'tradition': <Hindustani or Carnatic>,</p> <p> 'type': <vocal or instrumental><br> }</p> <p><br> where keys of the main dictionary are the filepaths to the audio files (feature path is exactly the same with a different extension of the file name).</p> <p><strong>Using this dataset</strong></p> <p>If you use this dataset in a publication, please cite:</p> <blockquote> <p>Gulati, S., Bellur, A., Salamon, J., Ranjani, H. G., Ishwar, V., Murthy, H. A., & Serra, X. (2014). Automatic Tonic Identification in Indian Art Music: Approaches and Evaluation. <em>Journal of New Music Research</em>, <em>43</em>(01), 55–73. </p> </blockquote> <p><a href="http://hdl.handle.net/10230/25675">http://hdl.handle.net/10230/25675</a></p> <p>We are interested in knowing if you find our datasets useful! If you use our dataset please email us at <a href="mailto:mtg-info@upf.edu">mtg-info@upf.edu</a> and tell us about your research.</p> <p><strong>Contact </strong></p> <p>If you have any questions or comments about the dataset, please feel free to email: [sankalp (dot) gulati (at) gmail (dot) com], or [sankalp (dot) gulati (at) upf (dot) edu]</p> <p> </p> <p><a href="http://compmusic.upf.edu/iam-tonic-dataset">http://compmusic.upf.edu/iam-tonic-dataset</a></p> <p> </p>
Jingju a cappella singing voice test dataset for "An efficient deep learning model for musical onset detection"
<p>Jingju a cappella singing voice test dataset used in the paper "An efficient deep learning model for musical onset detection".</p> <p>Arxiv paper link: <a href="https://arxiv.org/abs/1806.06773">https://arxiv.org/abs/1806.06773</a></p> <p>Supplementary information and code for the paper: <a href="https://github.com/ronggong/musical-onset-efficient">https://github.com/ronggong/musical-onset-efficient</a></p> <p><strong>Content:</strong></p> <ol> <li>ismir_2018_dataset_for_reviewing.zip: audio, syllable boundary and label annotation</li> <li>jingju dataset train test split filenames.xlsx: train and test split filename list</li> </ol> <p><strong>Citation:</strong></p> <pre>@article{gong2018towards, title={Towards an efficient deep learning model for musical onset detection}, author={Gong, Rong and Serra, Xavier}, journal={arXiv preprint arXiv:1806.06773}, year={2018} } </pre> <p><strong>Contact:</strong></p> <p>Rong Gong: rong.gong<at>upf.edu</p>
R code and dataset to "Monetizing Spillover Effects in the Creative Industries: the Impact of Live Music Performances on Youtube Searches"
<p>Content:</p> <ol> <li>The script<strong> main_script.R</strong> includes code to run a regression discontinuity (RD) design and validation and falsification of estimated results</li> <li>The folder <strong>data</strong> contains two files: <ol> <li>bands_2016_2019.csv: a dataset of performers with additional information for each one.</li> <li>festivals_2016_2019.csv: a dataset of video search activity (as retrieved from Google Trends) for performers in file bands_2016_2019.csv</li> </ol> </li> <li>The folder <strong>source</strong> contains two additional R scripts: <ol> <li>data_preparation.R: generates the long dataset used to estimate RD effects</li> <li>status_simulation.R: randomly assigns treattment status to performers and estimates RD effects. Note this may take a long time to run. Parallel code is used: the number of cores has been set to 4. </li> </ol> </li> <li>The folder simulation_results contains simulated data after running the script status_simulation.R.</li> </ol>
MGD+: An Enhanced Music Genre Dataset with Success-based Networks
<p>This dataset is built by using data from Spotify. It provides a daily chart of the 200 most streamed songs for each country and territory it is present, as well as an aggregated global chart. </p> <p>Considering that countries behave differently when it comes to musical tastes, we use chart data from global and regional markets from January 2017 to March 2022 (downloaded from CSV files), considering 68 distinct markets.</p> <p>We also provide information about the hit songs and artists present in the charts, such as all collaborating artists within a song (since the charts only provide the main ones) and their respective genres, which is the core of this work. MGD+ also provides data about musical collaboration, as we build collaboration networks based on artist partnerships in hit songs. Therefore, this dataset contains:</p> <ul> <li><strong>Genre Networks:</strong> Success-based genre collaboration networks</li> <li><strong>Artist Networks:</strong> Success-based artist collaboration networks</li> <li><strong>Artists:</strong> Some artist data</li> <li><strong>Hit Songs:</strong> Hit Song data and features</li> <li><strong>Charts:</strong> Enhanced data from Spotify Daily Top 200 Charts</li> </ul>
Milan 1958-1962: Music Topography of a City (main dataset)
<p>This data set contains information about 8288 music performances held in Milan from 1958 to 1962. It was collected from the "shows of the day" page on Italian newspapers <em>Il Giorno </em>and <em>Il Corriere della Sera. </em>Its collection, analysis and visualization is part of the PhD research project "Milan 1958-1962: Music Topography of a City". <a href="https://musictopography.github.io/">Project website</a></p>
TinySOL: an audio dataset of isolated musical notes
<p>TinySOL<br> =======<br> Version 6.0, February 2020.<br> </p> <p> </p> <p>Created By<br> --------------</p> <p>Carmine-Emanuele Cella (1), Daniele Ghisi (1), Vincent Lostanlen (2), Fabien Lévy (3), Joshua Fineberg (4), Yan Maresz (5)<br> <br> (1): UC Berkeley<br> (2): New York University<br> (3): Columbia University<br> (4): Boston University<br> (5): Conservatoire de Paris</p> <p> </p> <p>Description<br> ---------------</p> <p><br> TinySOL is a dataset of 2913 samples, each containing a single musical note from one of 14 different instruments:</p> <ol> <li>Bass Tuba</li> <li>French Horn</li> <li>Trombone</li> <li>Trumpet in C</li> <li>Accordion</li> <li>Contrabass</li> <li>Violin</li> <li>Viola</li> <li>Violoncello</li> <li>Bassoon</li> <li>Clarinet in B-flat</li> <li>Flute</li> <li>Oboe</li> <li>Alto Saxophone</li> </ol> <p> </p> <p>These sounds were originally recorded at Ircam in Paris (France) between 1996 and 1999, as part of a larger project named Studio On Line (SOL). Although SOL contains many combinations of mutes and extended playing techniques, TinySOL purely consists of sounds played in the so-called "ordinary" style, and in absence of mute.<br> <br> TinySOL can be used for creative purposes insofar at the use complies with the Creative Commons Attribution 4.0 International license (see below).<br> <br> TinySOL can be used for education and research purposes. In particular, it can be employed as a dataset for training and/or evaluating music information retrieval (MIR) systems, for tasks such as instrument recognition or fundamental frequency estimation. For this purpose, we provide an official 5-fold split of TinySOL. This split has been carefully balanced in terms of instrumentation, pitch range, and dynamics. For the sake of research reproducibility, we encourage users of TinySOL to adopt this split and report their results in terms of average performance across folds.</p> <p> </p> <p>Data Files<br> --------------</p> <p>TinySOL contains 2913 audio clips as WAV files, sampled at 44.1 kHz, with a single channel (mono), at a bit depth of 16. This is equivalent to the audio quality of a compact disc. Audio clips vary in duration between two and ten seconds.</p> <p>Every audio file has a file path of the form:<br> <FAMILY>/<INSTRUMENT>/ordinario/<INSTR>-ord-<PITCH>-<DYN>-<INSTANCE>-<MISC>.wav</p> <p><br> where:</p> <ul> <li><FAMILY> corresponds to the instrument family: "Brass", "Keyboards" (includes accordion), "Strings", and "Winds" (i.e., woodwinds).</li> <li><INSTRUMENT> is the full name of the instrument.</li> <li>"ordinario" denotes the ordinary playing technique. This is in contrast with the rest of the SOL dataset, which also encompasses extended playing techniques.</li> <li><INSTR> is the abbreviation of the instrument.</li> <li>"ord" is the abbreviation of "ordinario".</li> <li><PITCH> denotes the pitch of the musical note. This pitch is encoded in the American standard pitch notation: pitch class (C means "do") followed by pitch octave. According to this convention, A4 has a fundamental frequency of 440 Hz.</li> <li><DYN> denotes the intensity dynamics, ranked from pp (pianissimo) to ff (fortissimo).</li> <li><INSTANCE> contains additional information, when applicable. For example, for bowed string instruments, the same pitch may sometimes be achieved on different positions and different strings, resulting in small timbre differences. In this case the label "1c", "2c", "3c", or "4c" denotes the string which is being bowed. (The letter c originates from the word "corde", which means string in French.) By convention, the first string is the one with the highest pitch when played as an open string. Furthermore, on some wind instruments, the same note was played multiple times, e.g. at multiple durations. In this case, we use the label "alt1", "alt2", etc. to denote alternative instances of the note. If none of these tags apply, the <INSTANCE> field becomes "N", which stands for "Not Applicable".</li> <li><MISC> contains additional information, if applicable. In TinySOL, some pitches were never recorded (about 1% of the whole dataset), and thus missing from the chromatic scale. In this case, the <MISC> tag contains a letter "R", to denote the fact that the corresponding WAV file has been obtained by transforming a different audio clip via some digital frequency transposition (similar to Auto-Tune). The letter "R" stands for "resampled". Furthermore, some pitches (about 20% of the whole dataset) were slightly out of tune in comparison with the A440 tuning standard. Again, we applied some digital frequency transposition to correct them and put them exactly in tune. The amount of frequency transposition is measured in "cents" of an equal-tempered semitone. The letter "T" stands for "tuned". Because we employed a high-fidelity algorithm for frequency transposition, and because the amount of digital frequency transposition is small, the timbre of pitch-corrected notes remains faithful to the instrument. If none of these tags apply, the <MISC> field becomes "N", which stands for "natural"; in this case, the note is distributed exactly as it was recorded in the studio.</li> </ul> <p>For example, "Strings/Violin/ordinario/Vn-ord-D#7-mf-1c-T22d_R100u" corresponds to:</p> <ul> <li>a violin sound ;</li> <li>played in the ordinary playing technique ;</li> <li>at pitch D#7 (approximately 2489 Hz) ;</li> <li>with mezzoforte dynamics ;</li> <li>on the first string ; and</li> <li>resampled from a D7 by raising pitch by a semitone, i.e. 100 cents (R100u)</li> <li>lowered by 22 cents (T22d) to match the A440 tuning standard.</li> </ul> <p> </p> <p>Metadata File<br> -------------------</p> <p>The TinySOL_metadata.csv file contains 2913 rows, one for each audio clip. It can be opened by a text editor or by a spreadsheet software application. It contains 13 columns:</p> <ol> <li>Path to the WAV file, in UNIX filesystem format. For Windows compatibility, replace the slashes ("/") by backslashes ("\"). Ex: "Brass/BTb/BTb-ord-A#1-ff-N.wav"</li> <li>Fold ID. Either equal to 0, 1, 2, 3, or 4.</li> <li>Family. Ex: "Brass"</li> <li>Instrument abbreviation. Ex: "BTb"</li> <li>Instrument name in full. Ex: "Bass Tuba"</li> <li>Technique abbreviation. Always equal to "ord" in the case of TinySOL.</li> <li>Technique name in full. Always equal to "ordinario" in the case of TinySOL.</li> <li>Pitch. Ex: "A#1"</li> <li>Pitch ID in MIDI format. Ex: 34. Integer in the range 0-127.</li> <li>Dynamics. Ex: "ff".</li> <li>Dynamics ID. Integer. pp maps to 0 and ff maps to 4. The higher, the louder.</li> <li>Instance ID. Integer in the range 0-4</li> <li>String ID. Equal to 1, 2, 3, 4, or empty if not applicable.</li> <li>"Needed digital retuning". TRUE if the file has been pitch-shifted with digital audio effects; FALSE otherwise.</li> </ol> <p> </p> <p>Conditions of Use<br> ------------------------</p> <p>TinySOL was created in 2020 by Carmine-Emanuele Cella, Daniele Ghisi, Vincent Lostanlen, Fabien Lévy, Joshua Fineberg, and Yan Maresz.</p> <p>TinySOL is a derivative of SOL. We wish to thank Hugues Vinet, Greg Beller, and all coordinators of the Ircam Forum for their authorization to upload TinySOL to Zenodo.</p> <p>TinySOL is offered free of charge under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license:<br> https://creativecommons.org/licenses/by/4.0/</p> <p>The dataset and its contents are made available on an "as is" basis and without warranties of any kind, including without limitation satisfactory quality and conformity, merchantability, fitness for a particular purpose, accuracy or completeness, or absence of errors. Subject to any liability that may not be excluded or limited by law, the authors are not liable for, and expressly exclude all liability for, loss or damage however and whenever caused to anyone by any use of the TinySOL dataset or any part of it.</p> <p>We encourage TinySOL users to subscribe to the Ircam Forum so that they can have access to larger versions of SOL. While downloading full version of SOL requires premium membership (for a yearly fee), a medium-sized version named OrchideaSOL is made available free of charge to all members. Note, however, that TinySOL is the only subset of SOL which is released under a Creative Commons License. For more information, please visit: https://forum.ircam.fr/</p> <p> </p> <p>Versions<br> -----------<br> 1.0 was released on January 31st, 2020.<br> 2.0 and 3.0 were released the same day, after fixing an issue in the metadata related to file paths.<br> 4.0 was released on February 7th, 2020. The file structure of the tar.gz file was simplified so as to improve the interoperability with the mirdata Python package.<br> 5.0 was released on February 23th, 2020. New audio samples were added (from 2478 to 2913) and more details were supplied regarding retuning.<br> 6.0 was released the same day, after fixing an issue in the metadata related to file paths.</p> <p> </p> <p>Feedback<br> -------------</p> <p>Please help us improve TinySOL by sending your feedback to:<br> carmine.cella@berkeley.edu</p> <p>For issues regarding the metadata encoding, the five-fold split, or the TinySOL module in mirdata, please write to:<br> vincent.lostanlen@nyu.edu</p> <p>In case of a problem, please include as many details as possible.</p> <p> </p> <p> </p>
Saraga: research datasets of Indian Art Music
<p><strong>Dataset introduction</strong></p> <p>This repository contains time aligned melody, rhythm, and structural annotations for two large open corpora of Indian Art Music (Carnatic and Hindustani music).</p> <p>The repository contains Carnatic and Hindustani collections in separated zip files, and each collection is organized by songs grouped by artist concerts/live performances. This organization follows the structure generated by downloading the data using the scripts available at the dataset Github repository: <a href="https://github.com/MTG/saraga">https://github.com/MTG/saraga</a>.</p> <p>Moreover, there is a part of the Carnatic collection, 168 tracks to be specific, that counts with multitrack audio files apart from the mix audio. The considered instruments are: Ghatam, Mridangam, Violin, Voice and Secondary Voice.</p> <p> </p> <p><strong>Annotations in the dataset</strong></p> <p>Section and tempo annotations stored as start and end timestamps together with the name of the section and tempo during the section (in a separate file). Sama annotations referring to rhythmic cycle boundaries stored as timestamps. Phrase annotations stored as timestamps and transcription of the phrases using solfège symbols ({S, r, R, g, G, m, M, P, d, D, n, N}). Audio features automatically extracted and stored: pitch and tonic.</p> <p>For more information about the dataset tracks and annotations, please refer to the Saraga website: <a href="https://mtg.github.io/saraga/">https://mtg.github.io/saraga/</a></p> <p> </p> <p><strong>Using this dataset</strong></p> <p>We are interested in knowing if you find our datasets useful! If you use our dataset please email us at <a href="mailto:mtg-info@upf.edu">mtg-info@upf.edu</a> and tell us about your research.</p> <p>*Please note that you can also use this dataset through the MIRDATA library (<a href="https://github.com/mir-dataset-loaders/mirdata">https://github.com/mir-dataset-loaders/mirdata</a>), where this dataset is in the list of available datasets.</p>
lastfm Music Recommendation Dataset
<p>This is a common Zenodo repository for both<a href="http://ocelma.net/MusicRecommendationDataset/lastfm-360K.html"> lastfm-360K</a> and <a href="http://ocelma.net/MusicRecommendationDataset/lastfm-1K.html">lastfm-1K</a> datasets. See below the details of both datasets, including license, acknowledgements, contact, and instructions to cite.</p> <p> </p> <p><strong>LASTFM-360K (version 1.2, March 2010).</strong></p> <ul> <li><strong>What is this?</strong> This dataset contains <user, artist, plays> tuples (for ~360,000 users) collected from <a href="http://www.last.fm/api">Last.fm API</a>, using the <a href="http://www.last.fm/api/show?service=300">user.getTopArtists()</a> method.</li> <li><strong>Files:</strong> <ul> <li>usersha1-artmbid-artname-plays.tsv (MD5: be672526eb7c69495c27ad27803148f1)</li> <li>usersha1-profile.tsv (MD5: 51159d4edf6a92cb96f87768aa2be678)</li> <li>mbox_sha1sum.py (MD5: feb3485eace85f3ba62e324839e6ab39)</li> </ul> </li> <li><strong>Data Statistics:</strong> <ul> <li>File <em>usersha1-artmbid-artname-plays.tsv</em>: <ul> <li>Total Lines: 17,559,530</li> <li>Unique Users: 359,347</li> <li>Artists with <a href="http://musicbrainz.org/">MBID</a>: 186,642</li> <li>Artists without <a href="http://musicbrainz.org/">MBID</a>: 107,373</li> </ul> </li> </ul> </li> <li><strong>Data Format:</strong> The data is formatted one entry per line as follows (tab separated "\t"): <ul> <li>File <em>usersha1-artmbid-artname-plays.tsv</em>: <pre>user-mboxsha1 \t musicbrainz-artist-id \t artist-name \t plays</pre> </li> <li>File <em>usersha1-profile.tsv</em>: <pre>user-mboxsha1 \t gender (m|f|empty) \t age (int|empty) \t country (str|empty) \t signup (date|empty)</pre> </li> </ul> </li> <li><strong>Example:</strong> <ul> <li>File <em>usersha1-artmbid-artname-plays.tsv</em>: <pre>000063d3fe1cf2ba248b9e3c3f0334845a27a6be \t a3cb23fc-acd3-4ce0-8f36-1e5aa6a18432 \t u2 \t 31 ...</pre> </li> <li>File <em>usersha1-profile.tsv</em>: <pre>000063d3fe1cf2ba248b9e3c3f0334845a27a6be \t m \t 19 \t Mexico \t Apr 28, 2008 ...</pre> </li> </ul> </li> </ul> <p> </p> <p><strong>LASTFM-1K (version 1.0, March 2010).</strong></p> <ul> <li><strong>What is this?</strong> This dataset contains <user, timestamp, artist, song> tuples collected from <a href="http://www.last.fm/api">Last.fm API</a>, using the <a href="http://www.last.fm/api/show?service=278">user.getRecentTracks()</a> method. This dataset represents the whole listening habits (till May, 5th 2009) for nearly 1,000 users.</li> <li><strong>Files:</strong> <ul> <li>userid-timestamp-artid-artname-traid-traname.tsv (MD5: 64747b21563e3d2aa95751e0ddc46b68)</li> <li>userid-profile.tsv (MD5: c53608b6b445db201098c1489ea497df)</li> </ul> </li> <li><strong>Data Statistics:</strong> <ul> <li>File <em>userid-timestamp-artid-artname-traid-traname.tsv:</em> <ul> <li>Total Lines: 19,150,868</li> <li>Unique Users: 992</li> <li>Artists with MBID: 107,528</li> <li>Artists without MBDID: 69,420</li> </ul> </li> </ul> </li> <li><strong>Data Format:</strong> The data is formatted one entry per line as follows (tab separated, "\t"): <ul> <li>File <em>userid-timestamp-artid-artname-traid-traname.tsv</em>: <pre>userid \t timestamp \t musicbrainz-artist-id \t artist-name \t musicbrainz-track-id \t track-name</pre> </li> <li>File <em>userid-profile.tsv</em>: <pre>userid \t gender ('m'|'f'|empty) \t age (int|empty) \t country (str|empty) \t signup (date|empty)</pre> </li> </ul> </li> <li><strong>Example:</strong> <ul> <li>File <em>userid-timestamp-artid-artname-traid-traname.tsv</em>: <pre>user_000639 \t 2009-04-08T01:57:47Z \t MBID \t The Dogs D'Amour \t MBID \t Fall in Love Again? user_000639 \t 2009-04-08T01:53:56Z \t MBID \t The Dogs D'Amour \t MBID \t Wait Until I'm Dead ...</pre> </li> <li>File <em>userid-profile.tsv</em>: <pre>user_000639 \t m \t Mexico \t Apr 27, 2005 ...</pre> </li> </ul> </li> </ul> <p> </p> <p><strong>LICENSE OF BOTH DATASETS</strong>. The data contained in both datasets is distributed with permission of <a href="http://last.fm">Last.fm</a>. The data is made available for non-commercial use. Those interested in using the data or web services in a commercial context should contact:</p> <p><em>partners [at] last [dot] fm</em></p> <p>For more information see Last.fm <a href="http://www.last.fm/api/tos">terms of service</a></p> <p> </p> <p><strong>ACKNOWLEDGEMENTS. </strong>Thanks to Last.fm for providing the access to this data via their web services. Special thanks to <a href="http://www.last.fm/user/nova77LF">Norman Casagrande</a>.</p> <p> </p> <p><strong>REFERENCES. </strong>When using this dataset you must reference the <a href="http://last.fm">Last.fm</a> webpage. Optionally (not mandatory at all!), you can cite <em>Chapter 3</em> of <a href="http://ocelma.net/MusicRecommendationBook/index.html">this book</a>:</p> <pre>@book{Celma:Springer2010, author = {Celma, O.}, title = {{Music Recommendation and Discovery in the Long Tail}}, publisher = {Springer}, year = {2010} } </pre> <p> </p> <p><strong>CONTACT: </strong>This data was collected by <a href="http://ocelma.net/">Òscar Celma</a> @ <a href="http://mtg.upf.edu">MTG</a>/<a href="http://upf.edu">UPF</a></p>
Erkomaishvili Dataset: A Curated Corpus of Traditional Georgian Vocal Music for Computational Musicology
<p><strong>Abstract</strong></p> <p>The analysis of recorded audio material using computational methods has received increased attention in ethnomusicological research. We present a curated dataset of traditional Georgian vocal music for computational musicology. The corpus is based on historic tape recordings of three-voice Georgian songs performed by the the former master chanter Artem Erkomaishvili. In this article, we give a detailed overview on the audio material, transcriptions, and annotations contained in the dataset. Beyond its importance for ethnomusicological research, this carefully organized and annotated corpus constitutes a challenging scenario for music information retrieval tasks such as fundamental frequency estimation, onset detection, and score-to-audio alignment. The corpus is publicly available and accessible through score-following web-players.</p> <p><strong>License</strong></p> <p>This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.</p> <p><strong>Copyright of Audio (wav)</strong></p> <p>Ministry of Culture, Sports and Youth of Georgia<br> Legal Entity of Public Law<br> Vano Sarajishvili Tbilisi State Conservatoire (TSC)<br> 8-10, GRIBOEDOV St, TBILISI 0108, GEORGIA Tel. / fax :(+995 32) 2 999 144,<br> www.tsc.edu.ge E-mail: info@tsc.edu.ge; inter@tsc.edu.ge</p> <p>We thank the rector of TSC, Nana Sharikadze, for the permission to publish the recordings along with our annotations on Zenodo.</p> <p><strong>Copyright of Annotations (csv)</strong></p> <p>Sebastian Rosenzweig^1, Frank Scherbaum^2, David Shugliashvili^3, Vlora Arifi-Müller^1, and Meinard Müller^1<br> ^1: International Audio Laboratories Erlangen, Germany<br> ^2: University of Potsdam, Germany<br> ^3: Tbilisi State Conservatoire, Georgia</p> <p>The provided digital sheet music in MusicXML-format is based on the transcriptions by David Shugliashvili as published in the book:</p> <p>David Shugliashvili<br> Georgian Church Hymns, Shemokmedi School<br> Georgian Chanting Foundation, 2014.</p> <p><strong>References</strong></p> <p>If you use the Erkomaishvili dataset in your research, please cite:</p> <p>Sebastian Rosenzweig, Frank Scherbaum, David Shugliashvili, Vlora Arifi-Müller, and Meinard Müller<br> Erkomaishvili Dataset: A Curated Corpus of Traditional Georgian Vocal Music for Computational Musicology<br> Transactions of the International Society for Music Information Retrieval (TISMIR), 3(1): 31–41, 2020.</p>
IKA CI Pop Music Dataset
<p><strong>IKA CI Pop Music Dataset</strong></p> <p>The <strong>IKA CI Pop Music Dataset (IKA-CI-PMD)</strong> is a dataset of music excerpts that has especially compiled to evaluate different music signal preprocessing strategies for CI listeners.<br> It contains 15 pop music excerpts with a duration of approximately 12 s each that are taken from the MedleyDB dataset (<a href="https://medleydb.weebly.com/">https://medleydb.weebly.com/</a>) curated by <a href="https://steinhardt.nyu.edu/marl/research/resources/medleydb">Rachel Bittner et. al.</a>.<br> Like MedleyDB, it is licensed MedleyDB is licensed under a <a href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.<br> The excerpts have been selected such that they contain short self-contained phrases where vocals, bass, drums and other accompaniment are likewise present.<br> They also cover a range of different pop music subgenres and songs with both male and female lead vocals.</p> <p>The signals have been modified from the original MedleyDB versions. They have been remixed to equal loudness of vocals stem and all accompaniment stems where the mixing ratios between the accompaniment stems (bass, drums, other accompaniment) was retained.<br> Furthermore, the excerpts included in the dataset are normalized to a loudness level of -27 LUFS.<br> The signals are stored in the lossless FLAC format.<br> The files contain stereo signals, where both channels are identical.</p> <p>The dataset has been compiled at the Ruhr University Bochum <a href="https://www.ruhr-uni-bochum.de/ika/index_en.html">Institute of Communication Acoustics</a> in 2022 by Johannes Gauer (<a href="mailto:mailto:johannes.gauer@rub.de">johannes.gauer@rub.de</a>) in collaboration with the fellow researchers Anil Nagathil, Benjamin Lentz, and Rainer Martin.</p>
A Large TV Dataset for Speech and Music Activity Detection
<p>Automatic speech and music activity detection (SMAD) is an enabling task that can help segment, index, and pre-process audio content in radio broadcast and TV programs. However, due to copyright concerns and the cost of manual annotation, the limited availability of diverse and sizeable datasets hinders the progress of state-of-the-art (SOTA) data-driven approaches. We address this challenge by presenting a large-scale dataset containing Mel spectrogram, VGGish, and MFCCs features extracted from around 1600 hours of professionally produced audio tracks and their corresponding noisy labels indicating the approximate location of speech and music segments. The labels are derived from several sources such as subtitles. A test set curated by human annotators is also included as a subset for evaluation. To the best of our knowledge, this dataset is the first large-scale, open-sourced dataset that contains features extracted from professionally produced audio tracks and their corresponding frame-level speech and music annotations. </p>
Pipaset preview: A multimodal dataset for AMT and EA tasks dedicated to Chinese music instrument Pipa
<p>Yuancheng Wang, Yuyang Jing , Wei Wei, Dorian Cazau, Olivier Adam, Qiao Wang</p> <p>Accompanying <a href="http://github.com/yuanchengwang/TEAS">Website</a> here.</p> <p>If you make use of PipaSet for academic purposes, please cite the following publication:</p> <blockquote> <p>PipaSet and TEAS: A Multimodal Dataset and Annotation Platform for Automatic Music Transcription and Expressive Analysis dedicated to Chinese Traditional Plucked String Instrument Pipa. IEEE ACCESS 2022.</p> </blockquote> <p>This project was led by Yuancheng Wang at Information School of Information Science and Engineering, Southeast University, China, along with my supervisor Prof. Qiao Wang from same school and Dr. Yuyang Jing from Nanjing University of the Arts, Wei Wei form Xiaozhuang University, Dr. Dorian Cazau from Institute of Mines-Télécom Atlantique in Brest France, Prof. Olivier Adam from Sorbonne University.</p> <p>We present PipaSet, a dataset that provides multimodal pipa recordings alongside a high diversity of annotations for Automatic Music Transcription and Expressive Analysis tasks, including note, pitch contours, string and fret positions, and playing techniques. </p> <p>More information will be coming soon to cover more pieces of music played by pipa. </p>
ORCHSET: a dataset for melody extraction in symphonic music recordings
<p>Orchset is intended to be used as a dataset for the development and evaluation of melody extraction algorithms. This collection contains 64 audio excerpts focused on symphonic music. with their corresponding annotation of the melody.</p> <p>Melody is here defined as “the single (monophonic) pitch sequence that a listener might reproduce if asked to whistle or hum a piece of polyphonic music”.</p> <p>The dataset creation comprised several tasks: excerpts selection, recording sessions of people singing along with the excerpts, analysis of the recordings and melody annotation. A complete description of the dataset and the creation methodology is presented in this paper:</p> <blockquote> <p>Bosch, J., Marxer, R., Gomez, E., “Evaluation and Combination of Pitch Estimation Methods for Melody Extraction in Symphonic Classical Music”, Journal of New Music Research (2016)</p> </blockquote> <p>Please Acknowledge Orchset in Academic Research</p> <p><strong>Using this dataset</strong></p> <p>When Orchset is used for academic research, we would highly appreciate if scientific publications of works partly based on the Orchset dataset quote the above publication.</p> <p>We are interested in knowing if you find our datasets useful! If you use our dataset please email us at <a href="mailto:mtg-info@upf.edu">mtg-info@upf.edu</a> and tell us about your research.</p> <p> </p> <p><a href="https://www.upf.edu/web/mtg/orchset">https://www.upf.edu/web/mtg/orchset</a></p>
IRMAS: a dataset for instrument recognition in musical audio signals
<p>This dataset includes musical audio excerpts with annotations of the predominant instrument(s) present. It was used for the evaluation in the following article:</p> <blockquote> <p>Bosch, J. J., Janer, J., Fuhrmann, F., & Herrera, P. “<a href="http://ismir2012.ismir.net/event/papers/559_ISMIR_2012.pdf">A Comparison of Sound Segregation Techniques for Predominant Instrument Recognition in Musical Audio Signals</a>”, in Proc. ISMIR (pp. 559-564), 2012</p> </blockquote> <p>Please Acknowledge IRMAS in Academic Research</p> <p>IRMAS is intended to be used for training and testing methods for the automatic recognition of predominant instruments in musical audio. The instruments considered are: cello, clarinet, flute, acoustic guitar, electric guitar, organ, piano, saxophone, trumpet, violin, and human singing voice. This dataset is derived from the one compiled by Ferdinand Fuhrmann in his <a href="http://www.dtic.upf.edu/~ffuhrmann/PhD/">PhD thesis</a>, with the difference that we provide audio data in stereo format, the annotations in the testing dataset are limited to specific pitched instruments, and there is a different amount and lenght of excerpts.</p> <p><strong>Using this dataset</strong></p> <p>When IRMAS is used for academic research, we would highly appreciate if scientific publications of works partly based on the IRMAS dataset quote the above publication.</p> <p>We are interested in knowing if you find our datasets useful! If you use our dataset please email us at <a href="mailto:mtg-info@upf.edu">mtg-info@upf.edu</a> and tell us about your research.</p> <p> </p> <p><a href="https://www.upf.edu/web/mtg/irmas">https://www.upf.edu/web/mtg/irmas </a></p>
Dataset for Evaluating Sustain-Pedal Detection from Polyphonic Piano Music
<p>To evaluate methods of sustain-pedal detection from polyphonic piano music, we built a dataset consisting of ten well-known passages of Chopin's music. Ground-truth annotations of this dataset represent sustain-pedal on/off states at every 0.1 second. This annotation was based on the sustain-pedal movement tracked by a dedicated measurement system.</p> <p>Music scores of the ten passages were saved in PDF files. They were performed by a pianist using a Yamaha baby grand piano situated in the studios at Queen Mary University of London. The audio were recorded at 44.1 kHz and 24 bits using the spaced-pair stereo microphone technique. A pair of Earthworks QTC40 omnidirectional condenser microphones was positioned about 50 cm above the strings. </p> <p>We have developed a transfer learning method such that the on/off state of the sustain pedal can be detected at every 0.1 second. The ground-truth annotation and our detection results were saved in <em>transfer-learning-y_segment.npz</em>, which can be loaded using <em>numpy.load </em>in Python. The key for passage name, ground-truth annotation and our detection results is <em>filename_record</em>, <em>y_true</em> and <em>y_pred</em>, respectively.</p>
Emotion Painting Music Dataset
<p>The dataset contains paired painting-audio dataset based on five different emotions - angry, sad, neutral, fun, happy.</p>
Data from: Creating a multi-track classical music performance dataset for multi-modal music analysis: challenges, insights, and applications
We introduce a dataset for facilitating audio-visual analysis of musical performances. The dataset comprises 44 simple multi-instrument classical music pieces assembled from coordinated but separately recorded performances of individual tracks. For each piece, we provide the musical score in MIDI format, the audio recordings of the individual tracks, the audio and video recording of the assembled mixture, and ground- truth annotation files including frame-level and note-level tran- scriptions. We describe our methodology for the creation of the dataset, particularly highlighting our approaches for addressing the challenges involved in maintaining synchronization and ex- pressiveness. We demonstrate the high quality of synchronization achieved with our proposed approach by comparing the dataset against existing widely-used music audio datasets. We anticipate that the dataset will be useful for the devel- opment and evaluation of existing music information retrieval (MIR) tasks, as well as for novel multi-modal tasks. We bench- mark two existing MIR tasks (multi-pitch analysis and score- informed source separation) on the dataset and compare against other existing music audio datasets. Additionally, we consider two novel multi-modal MIR tasks (visually informed multi-pitch analysis and polyphonic vibrato analysis) enabled by the dataset and provide evaluation measures and baseline systems for future comparisons (from our recent work). Finally, we propose several emerging research directions that the dataset enables.
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