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1,612 results for “Music”
Cadenza Challenge (CAD2): databases for rebalancing classical music task
<h1>Cadenza</h1> <p>Please, cite CadenzaWoodwind as</p> <blockquote> <p><strong>Gerardo Roa-Dabike , Trevor J. Cox , Alex J. Miller , Bruno M. Fazenda , Simone Graetzer , Rebecca R. Vos , Michael A. Akeroyd , Jennifer Firth , William M. Whitmer , Scott Bannister , Alinka Greasley , Jon P. Barker , The Cadenza Woodwind Dataset: Synthesised Quartets for Music Information Retrieval and Machine Learning, Data in Brief (2024), doi: https://doi.org/10.1016/j.dib.2024.111199</strong></p> </blockquote> <p>This is the training and validation data for the rebalancing classic music task from the <a href="https://cadenzachallenge.org/">Second Cadenza Machine Learning Challenge (CAD2).</a></p> <p>The Cadenza Challenges are improving music production and processing for people with a hearing loss. According to The World Health Organization, 430 million people worldwide have a disabling hearing loss. Hearing aid users report several issues when listening to music, including distortion in the bass, difficulties in perceiving the full range of the music, especially high-frequency pitches, and a tendency to miss the impact of quieter parts of compositions [1]. In a pilot study, we found giving listeners sliders to allow them to rebalance different instruments in a classical music ensemble was desirable.</p> <p>Overview of files:</p> <ol> <li>CadenzaWoodwind. Synthesized dataset of small ensembles of woodwind instruments for training and validation.</li> <li>EnsembleSet_Mix_1. A subset of the synthesised <a href="../records/6519024">EnsembleSet [7]</a> for training and validation (Mix_1 render).</li> <li>Real Data for Tuning: <a href="../api/records/12664932/draft/files/Stereo_Reverb_Real_Data_For_Tuning.zip/content" target="_blank" rel="noopener noreferrer">Stereo_Reverb_Real_Data_For_Tuning.zip</a>.</li> <li>metadata.zip contains audiograms, scene details, target gains and compressor settings.</li> </ol> <p>The audio files are in FLAC format in the .zip archives. The json files contain metadata.</p> <p>More details below.</p> <p> </p>
EEG: Improvisation and Musical Structures
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Functional Connectivity of Music-Induced Analgesia in Fibromyalgia
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A dataset recorded during development of an affective brain-computer music interface: calibration session
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A dataset recorded during development of an affective brain-computer music interface: testing session
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A dataset recorded during development of an affective brain-computer music interface: training sessions
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Open Database of Spatial Room Impulse Responses at Detmold University of Music
<p>This repository contains an open source database of Spatial Room Impulse Responses (SRIR) captured at three different performance spaces of the Detmold University of Music. It includes the following rooms: </p> <ul> <li>Detmold Konzerthaus (medium sized concert hall, ~600 seats).</li> <li>Brahmssaal (small music chamber room, ~100 seats).</li> <li>Detmold Sommertheater (theater, ~300 seats).</li> </ul> <p>The collection contains approximately 600 multichannel RIRs corresponding to several source and receiver configurations. For each room we include measurement positions on stage and at the audience area captured with both an artificial head and an open microphone array compatible with the Spatial Decomposition Method (SDM).</p> <p>The Detmold Konzerthaus holds a large scale Wave Field Synthesis system and a Room Acoustic Enhancement System. SRIRs of an ensemble of focused sources on stage and with conditions of increased artificial reverberation are also included.</p> <p>If you use this dataset for your research, please cite our work:</p> <p>Amengual Gari, S. V.; Sahin, B.; Eddy, D; Kob, M.: <strong>"Open Database of Spatial Room Impulse Responses at Detmold University of Music"</strong>, <em>149th Convention of the Audio Engineering Society, </em>2020.</p> <p> </p> <p>The database is organized in 3 sets:</p> <p><strong>- Set A: </strong></p> <p>Source: Single Source measurements.</p> <p>Receiver: Open Array and Dummy Head.</p> <p>Rooms: BS, DST, KH</p> <p>Special configurations: Artificial reverberation, music stand on stage</p> <p><strong>- Set B: </strong></p> <p>Source: Loudspeaker and WFS orchestra</p> <p>Receiver: Open Array.</p> <p>Rooms: KH</p> <p><strong>- Set C:</strong></p> <p>Source: Loudspeaker orchestra</p> <p>Receiver: Dummy Head and Omni8 array</p> <p>Rooms: KH</p> <p> </p> <p>Further details on the measurement procedure and acoustical analysis of the RIRs can be found in the following publications:</p> <p><strong>Set A</strong></p> <p>Amengual Gari, S. V., Investigations on the Influence of Acoustics on Live Music Performance using Virtual Acoustic Methods, Ph.D. thesis, 2017.</p> <p>Amengual Garí, S. V.; Kob, M: "Investigating the impact of a music stand on stage using spatial impulse responses". 142nd Convention of the Audio Engineering Society, Berlin, May 2017.</p> <p><strong>Set B</strong></p> <p>Amengual Garí, S. V.; Pätynen, J.; Lokki, T.: "Physical and perceptual comparison of real and focused sound sources in a concert hall". Journal of the Audio Engineering Society, vol. 64 (12), pp. 1014-1025, December 2016.</p> <p><strong>Set C</strong></p> <p>Sahin, B., ““Investigation of the Detmold Concert Hall auditorium acoustics by comparing preference ratings and objective measurements.”, M.Sc. Thesis, 2017.</p> <p>Sahin, B., Amengual, S. V., and Kob, M., “Investigating listeners’ preferences in Detmold Concert Hall by comparing sensory evaluation and objective measurements,” Proc. 43th DAGA, Kiel, 2017.<br> </p>
Perceptions of Diversity in Electronic Music: the Impact of Listener, Artist, and Track Characteristics
<p>Data Release and facsimile of the survey, presented in the submission 3238 to the CSCW 2021 conference.</p> <p> </p>
Record Label & Music Publishing Turnover in Europe
<p>Imputed and forecasted values of the recording and music publishing industry from the <a href="https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=sbs_na_1a_se_r2&lang=en">Annual detailed enterprise statistics for services (NACE Rev. 2 H-N and S95)</a> Eurostat folder.</p>
FolkArtiNet: Folk music groups: their artistic practice and infrastructural needs in the COVID-19 era and beyond - survey data
<p> The online survey was one of three methods used for collecting information about the infrastructural needs of the folk music groups. It included a series of questions about different areas of artistic activity, such as working on repertoire, collaboration among group members, storage and sharing of data, and organization of artistic events. The survey data includes all questions and answers in csv format.</p>
The Italian Music Dataset
<p><strong>Overview</strong></p> <p>The dataset is built by exploiting the Spotify and SoundCloud APIs. It is composed of over 14,500 different songs of both famous and less famous Italian musicians. Each song in the dataset is identified by its Spotify id and its title. Tracks' metadata include also lemmatized and POS-tagged lyrics and, in the most of cases, ten musical features directly gathered from Spotify. Musical features include acousticness (float), danceability (float), duration_ms (int), energy (float), instrumentalness (float), liveness (float), loudness (float), speechiness (float), tempo (float) and valence (float). All features range from 0.0 to 1.0 except for loudness that typically ranges between -60 and 0 db, the tempo that represents beats per minute (BPM) and the duration that represents the track in milliseconds. For further information refer to the Spotify's documentation at <a href="https://developer.spotify.com/documentation/web-api/reference/tracks/get-audio-features/">Spotify Documentation</a></p> <p>For further information regarding the dataset and the related project visit <a href="https://bit.ly/2MUUwEx">SoBigData Catalogue</a></p>
Medley-solos-DB: a cross-collection dataset for musical instrument recognition
<p>Medley-solos-DB<br> =============<br> Version 1.2 March 2019.<br> </p> <p> </p> <p>Created By<br> --------------</p> <p>Vincent Lostanlen (1), Carmine-Emanuele Cella (2), Rachel Bittner (3), Slim Essid (4).<br> <br> (1): New York University<br> (2): UC Berkeley<br> (3): Spotify, Inc.<br> (4): Télécom ParisTech</p> <p> </p> <p><br> Description<br> ---------------</p> <p> </p> <p>Medley-solos-DB is a cross-collection dataset for automatic musical instrument recognition in solo recordings. It consists of a training set of 3-second audio clips, which are extracted from the MedleyDB dataset of Bittner et al. (ISMIR 2014) as well as a test set set of 3-second clips, which are extracted from the solosDB dataset of Essid et al. (IEEE TASLP 2009). Each of these clips contains a single instrument among a taxonomy of eight: clarinet, distorted electric guitar, female singer, flute, piano, tenor saxophone, trumpet, and violin.</p> <p>The Medley-solos-DB dataset is the dataset that is used in the benchmarks of musical instrument recognition in the publications of Lostanlen and Cella (ISMIR 2016) and Andén et al. (IEEE TSP 2019).</p> <p> </p> <p>[1] V. Lostanlen, C.E. Cella. Deep convolutional networks on the pitch spiral for musical instrument recognition. Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), 2016.</p> <p>[2] J. Andén, V. Lostanlen, and S. Mallat. Joint time-frequency scattering. IEEE Transactions in Signal Processing, vol. 67, no. 14, pp. 3704-3718, 2019. doi: 10.1109/TSP.2019.2918992</p> <p> </p> <p><br> Data Files<br> --------------</p> <p>The Medley-solos-DB contains 21571 audio clips as WAV files, sampled at 44.1 kHz, with a single channel (mono), at a bit depth of 32. Every audio clip has a fixed duration of 2972 milliseconds, that is, 65536 discrete-time samples.</p> <p>Every audio file has a name of the form:</p> <p>Medley-solos-DB_SUBSET-INSTRUMENTID_UUID.wav</p> <p> </p> <p>For example:</p> <p>Medley-solos-DB_test-0_0a282672-c22c-59ff-faaa-ff9eb73fc8e6.wav</p> <p>corresponds to the snippet whose universally unique identifier (UUID) is 0a282672-c22c-59ff-faaa-ff9eb73fc8e6, contains clarinet sounds (clarinet has instrument id equal to 0), and belongs to the test set.</p> <p> </p> <p><br> Metadata Files<br> -------------------</p> <p>The Medley-solos-DB_metadata is a CSV file containing 21572 rows (one for each audio clip) and five columns:</p> <p>1. subset: either "training", "validation", or "test"</p> <p>2. instrument: tag in Medley-DB taxonomy, such as "clarinet", "distorted electric guitar", etc.</p> <p>3. instrument id: integer from 0 to 7. There is a one-to-one between "instrument" (string format) and "instrument id" (integer). We provide both for convenience.</p> <p>4. song id: integer from 0 to 226. The track and artist names are anonymized.</p> <p>5. UUID4: universally unique identifier. Assigned and random, and different for every row.</p> <p> </p> <p>The list of instrument classes is:</p> <p>0. clarinet</p> <p>1. distorted electric guitar</p> <p>2. female singer</p> <p>3. flute</p> <p>4. piano</p> <p>5. tenor saxophone</p> <p>6. trumpet</p> <p>7. violin</p> <p> </p> <p><br> Please acknowledge Medley-solos-DB in academic research<br> ---------------------------------------------------------------------------------</p> <p>When Medley-solos-DB is used for academic research, we would highly appreciate it if scientific publications of works partly based on this dataset cite the following publication:</p> <p>V. Lostanlen, C.E. Cella. Deep convolutional networks on the pitch spiral for musical instrument recognition. Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), 2016.</p> <p>The creation of this dataset was supported by ERC InvariantClass grant 320959.</p> <p> </p> <p><br> Conditions of Use<br> ------------------------</p> <p>Dataset created by Vincent Lostanlen, Rachel Bittner, and Slim Essid, as a derivative work of Medley-DB and solos-Db.</p> <p>The Medley-solos-DB dataset 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 Medley-solos-DB dataset or any part of it.</p> <p> </p> <p><br> Feedback<br> -------------</p> <p>Please help us improve Medley-solos-DB by sending your feedback 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> <p>Acknowledgement<br> -------------------------<br> We thank all artists, recording engineers, curators, and annotators of both MedleyDB and solosDb.</p>
MAD-EEG: an EEG dataset for decoding auditory attention to a target instrument in polyphonic music
<p>The <em><strong>MAD-EEG Dataset</strong></em> is a research corpus for studying EEG-based auditory attention decoding to a target instrument in polyphonic music. </p> <p>The dataset consists of 20-channel EEG responses to music recorded from 8 subjects while attending to a particular instrument in a music mixture. </p> <p>For further details, please refer to the paper: <em><a href="https://hal.archives-ouvertes.fr/hal-02291882/document">MAD-EEG: an EEG dataset for decoding auditory attention to a target instrument in polyphonic music</a>.</em></p> <p>If you use the data in your research, please reference the paper (not just the Zenodo record):</p> <pre><code>@inproceedings{Cantisani2019, author={Giorgia Cantisani and Gabriel Trégoat and Slim Essid and Gaël Richard}, title={{MAD-EEG: an EEG dataset for decoding auditory attention to a target instrument in polyphonic music}}, year=2019, booktitle={Proc. SMM19, Workshop on Speech, Music and Mind 2019}, pages={51--55}, doi={10.21437/SMM.2019-11}, url={http://dx.doi.org/10.21437/SMM.2019-11} }</code></pre> <p> </p>
MUSIC-haic_WP1_T11_ACRT_01_ParticleImpactOnRigidSurface
<p>This dataset of ice particle impact onto a rigid surface was generated within work package 1 of the EU project MUSIC-haic. The project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 767560.</p> <p>A description of the experimental setup and methodology can be found in: Karpen et al. (2021), <em>Characterizing Microscopic Ice Particle Impacts onto a Rigid Surface: Wind Tunnel Setup and Analysis, </em>AIAA AVIATION 2021 FORUM, <a href="https://doi.org/10.2514/6.2021-2671">https://doi.org/10.2514/6.2021-2671</a></p>
How Clarinettists Play Music to Acheive Expressive Goals
<p>Dataset used in article 'How Clarinettists play Music to Acheive Expressive Goals' submitted to New Music Research on July 2023</p>
A dataset recorded during development of a tempo-based brain-computer music interface
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A dataset recording joint EEG-fMRI during affective music listening
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Neural mechanisms of musical syntax and tonality, and the effect of musicianship
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Temporal Dynamics of Emotional Music
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Culture-Aware Music Recommendation Dataset
<p><strong>LFM-1b dataset extended by acoustic track features and cultural cues describing users</strong></p> <p> </p> <p>This dataset is based on the LFM-1b dataset (cf. <a href="http://www.cp.jku.at/datasets/LFM-1b/">http://www.cp.jku.at/datasets/LFM-1b/</a>), however, adds acoustic features describing the tracks to the original dataset as well as cultural aspects describing users (taken from Hofstede's six dimension model and the World Happiness Report) on the country-level.</p> <p>For the creation of the dataset, we extract all users for which the original dataset contains country information for. We extract the listening events of these users and match the tracks against the Spotify API to subsequently retrieve the acoustic features of these tracks (cf. [Spotify Audio Feature Description](https://developer.spotify.com/documentation/web-api/reference/object-model/#audio-features-object)). The final dataset contains only events of users with country information and tracks with acoustic features, which can be matched with the country-level data of the World Happiness Report and Hofstede's cultural dimensions to add cultural and socio-economic aspects for users.</p> <p>This new dataset contains</p> <ul> <li>55,190 users</li> <li>3,471,884 tracks including acoustic features</li> <li>351,469,333 listening events of those users for tracks we have obtained acoustic features for</li> <li>Hofstede's cultural dimensions for 47 countries</li> <li>World Happiness Report (WHR) data for 164 countries</li> </ul> <p> </p> <p><strong>Files</strong><br> All files are tab-separated, with no quoting of strings. The dataset contains the following files, whose content we describe in more detail in the following parts.</p> <p>* acoustic_features_lfm_id.tsv: acoustic features for all tracks in the dataset, identified by their LFM track identifier<br> * events.tsv: listening events for all users<br> * hofstede.tsv: Hofstede's cultural dimensions<br> * users.tsv: user metadata<br> * world_happiness_report_2018.tsv: World Happiness Report data</p> <p>For further information on the contents of these files, please cf. the Readme file.</p> <p> </p> <p>Please cite the following paper when using the dataset:<br> Zangerle, E., Pichl, M. and Schedl, M., 2020. User Models for Culture-Aware Music Recommendation: Fusing Acoustic and Cultural Cues. <em>Transactions of the International Society for Music Information Retrieval</em>, 3(1), pp.1–16. DOI: <a href="http://doi.org/10.5334/tismir.37">http://doi.org/10.5334/tismir.37</a></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.