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Dataset results
12 results for “computer music”
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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A dataset recorded during development of a tempo-based brain-computer music interface
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Music Data Sharing Platform for Computational Musicology Research (CCMUSIC DATASET)
<p>This platform is a multi-functional music data sharing platform for Computational Musicology research. It contains many music datas such as the sound information of Chinese traditional musical instruments and the labeling information of Chinese pop music, which is available for free use by computational musicology researchers.</p> <p>This platform is also a large-scale music data sharing platform specially used for Computational Musicology research in China, including 3 music databases: Chinese Traditional Instrument Sound Database (CTIS), Midi-wav Bi-directional Database of Pop Music and Multi-functional Music Database for MIR Research (CCMusic). All 3 databases are available for free use by computational musicology researchers. For the contents contained in the database, we will provide audio files recorded by the professional team of the conservatory of music, as well as corresponding labelled files, which have no commodity copyright problem and facilitate large-scale promotion. We hope that this music data sharing platform can meet the one-stop data needs of users and contribute to the research in the field of Computational Musicology.</p> <p> </p> <p>If you want to know more information or obtain complete files, please go to the official website of this platform:</p> <p><a href="https://ccmusic-database.github.io/en/">Music Data Sharing Platform for Academic Research</a></p> <p> </p> <ul> <li> <p><strong>Chinese Traditional Instrument Sound Database (CTIS)</strong></p> </li> </ul> <p>This database is developed by Prof. Han Baoqiang's team for many years, which collects sound information about Chinese traditional musical instruments. The database includes 287 Chinese national musical instruments, including traditional musical instruments, improved musical instruments and ethnic minority musical instruments.</p> <ul> <li> <p><strong>Multi-functional Music Database for MIR Research</strong></p> </li> </ul> <p>This database collects sound materials of pop music, folk music and hundreds of national musical instruments, and makes comprehensive annotation to form a multi-purpose music database for MIR researchers.</p> <ul> <li><strong>Midi-wav Bi-directional Database of Pop Music</strong></li> </ul> <p>This database contains hundreds of Chinese pop songs, and each song contains the corresponding midi-audio-lyric information. Among them, recording the vocal part and accompaniment part of audio independently is helpful to study the MIR task under the ideal situation. In addition, the information of singing techniques consistent with vocal part (such as breath sound, falsetto, breathing, vibrato, mute, slide, etc.) is marked in MuseScore, which constitutes a Midi-Wav bi-direction corresponding pop music database.</p>
Deep Gradient Reinforcement learning for Music Improvisation in cloud computing framework
<p><span>The improvised music is further rendered in the MIDI format. The Bach Chorales dataset with six different attributes relevant to musical compositions is employed in implementing the present research. The model was set up in a containerised cloud environment and controlled for smooth load distribution. Five different parameters, such as pitch frequency (PF), standard pitch delay (SPD), average distance between peaks (ADP), note duration gradient (NDG) and pitch class gradient (PCG) are leveraged to assess the quality of the improvised music.</span></p>
Leveraging diversity in computer-aided musical orchestration with an artificial immune system for multi-modal optimization
<p>Data resulting from the experiments described in "Leveraging diversity in computer-aided musical orchestration with an artificial immune system for multi-modal optimization" (https://doi.org/10.1016/j.swevo.2018.12.010). The contents of the files is the following:</p> <ul> <li>CAMO-AIS_SWEVO.zip: All of the data below in a single file</li> <li>CAMO_Iowa.zip: Audio and Data for the orchestrations with the Iowa sound database found at http://theremin.music.uiowa.edu/MIS.html</li> <li>CAMO_Phil.zip: Audio and Data for the orchestrations with the Philharmonia sound database found at https://www.philharmonia.co.uk/explore/sound_samples</li> <li>CAMO_RWC.zip: Audio and Data for the orchestrations with the RWC sound database found at https://staff.aist.go.jp/m.goto/RWC-MDB/rwc-mdb-i.html</li> <li>CAMO_SOL.zip: Audio and Data for the orchestrations with the Studio Online sound database available with Orchids http://forumnet.ircam.fr/product/orchids-en/</li> <li>Listening_Test.zip: Raw data (i.e., perceptual similarity ratings) from the listening test found at http://http://camo.inesctec.pt/. This data is anonymous (each participant is assigned a reference number) so the participants cannot be identified.</li> </ul> <p>See the README.txt file for a detailed description of the contents of each file.</p>
Synesthetic: Music for Marimba and Computer-Controlled Lighting
<p>Synesthetic is an artistic project for percussion, electronics, composition, and lighting based in Oslo, Norway. The project aims to develop performative links between stage lighting, marimba and performer motions through software, hardware and composition. These videos are documentation of the initial project development week at NyDans, Oslo, in 2017 which resulted in five sketches exploring different sound/motion/lighting interactions.</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>
Letting Children Listen to Music During Computed Tomography
ClinicalTrials.gov study NCT06086509. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Computational Musicology – Distant Reading of Sheet Music
<p>2<sup>nd </sup>Lecture</p>
Pilot and Descriptive Study of the Effects of Setting up Computer-assisted Music (CAM) Remediation Groups on the Cognitive Functioning of Young Patients With a First Episode Psychosis (PEP)
ClinicalTrials.gov study NCT05880719. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.