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52 results for “music data”
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>
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>
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>
Data and scripts for 89 DJs at Ultra Music Festival 2015
<p>Datasets for "How to think music with Data" - Chapter 7.</p> <p>Contains:<br> Raw features: (djset2_mirtoolbox.gz, djset_mirtoolbox.gz)<br> MATLAB-scripts.<br> Excel spreadsheet (480DJSubsets.xlsx)<br> Tableau visualization files (480DJsubsetsIntervals.twb, 480DJsubsets.twb)</p> <p> </p> <p> </p>
An SEM Approach to Validating the Psychological Model of Musical Groove (Data Set)
<p>Data set for the study "An SEM Approach to Validating the Psychological Model of Musical Groove"</p>
Musical instrument playing with the Parametric hand data
<p>Data collected and source files for generation. Instructions on use and links to resources in README.txt</p> <p>Robot trajectories, logs, waveforms and loadcell data for experiments in embodied intelligence during music playing with the Parametric robot hand.</p>
Slow motion in films and video clips: Music influences perceived duration and emotion, autonomic physiological activation and pupillary responses [data set]
<p>Data set for a study to be published by PLOS ONE.</p>
Freischütz Digital: data-music, The Academy 2017 Release
<p>This is a copy of the <a href="https://github.com/Freischuetz-Digital/data-music" target="_blank" rel="noopener">Freischütz Digital/data-music</a> repository, which is meant for long-term preservation. A description of the dataset can be found in the <em>readme.md</em> file. </p>
TYPES: Male holotype from Panama: Panama: Parque Nacional Altos de Campana, 1 hectare PANCODING Inventory, 895 m, 8.68333°, -79.92972°, June 14–19, 2007, M. Arnedo, D. Dimitrov, G. Hormiga, F. Labarque, M. Ramírez, deposited in MIUP, PBI_OON 42313; same data, 1 male paratype deposited in MACN-Ar 29895, PBI_OON 42312. ETYMOLOGY: A noun in apposition; in Greek religion and mythology, Pan is the god of the wild natural world, of shepherds, flocks, and mountains, and of hunting and rustic music. He has hindquarters, legs, and horns of a goat, and the name is here employed to note the large mac- rosetae at the eye region of males that resemble the horns in some illustrations of this god. DIAGNOSIS: This is one of the most autapomor- phic species from the Americas; males have the labium fused with the sternum (fig. 34B), small chelicerae, shorter than the endite length, with anterior blunt projections, and directed backward in lateral view (fig. 34D, E); clypeus directed back- ward (fig. 34D); two light areas on the sternum just below the endites (fig. 34B), carapace almost flat in lateral view and two strong macrosetae at the eye region, pointing forward (fig. 34C–E). Other characters of the male palp, such as the presence of two apophyses, also distinguish this species from others (fig. 38D–F). MALE (PBI_OON 42312): Total length 1.00. Habitus as in figure 34A–C. CEPHALOTHO- RAX: Carapace orange, with brown stripe along in Taxonomic Revision Of The Jumping Goblin Spiders Of The Genus Orchestina Simon, 1882, In The Americas (Araneae: Oonopidae)
TYPES: Male holotype from Panama: Panama: Parque Nacional Altos de Campana, 1 hectare PANCODING Inventory, 895 m, 8.68333°, -79.92972°, June 14–19, 2007, M. Arnedo, D. Dimitrov, G. Hormiga, F. Labarque, M. Ramírez, deposited in MIUP, PBI_OON 42313; same data, 1 male paratype deposited in MACN-Ar 29895, PBI_OON 42312. ETYMOLOGY: A noun in apposition; in Greek religion and mythology, Pan is the god of the wild natural world, of shepherds, flocks, and mountains, and of hunting and rustic music. He has hindquarters, legs, and horns of a goat, and the name is here employed to note the large mac- rosetae at the eye region of males that resemble the horns in some illustrations of this god. DIAGNOSIS: This is one of the most autapomor- phic species from the Americas; males have the labium fused with the sternum (fig. 34B), small chelicerae, shorter than the endite length, with anterior blunt projections, and directed backward in lateral view (fig. 34D, E); clypeus directed back- ward (fig. 34D); two light areas on the sternum just below the endites (fig. 34B), carapace almost flat in lateral view and two strong macrosetae at the eye region, pointing forward (fig. 34C–E). Other characters of the male palp, such as the presence of two apophyses, also distinguish this species from others (fig. 38D–F). MALE (PBI_OON 42312): Total length 1.00. Habitus as in figure 34A–C. CEPHALOTHO- RAX: Carapace orange, with brown stripe along
Figures and data for the paper Perceptual Evaluation of Source Separation for Remixing Music
<p>Listening test results and figures for the paper:</p> <p>H. Wierstorf, D. Ward, R. Mason, E. M. Grais, C. Hummersone, M. D. Plumbley,<br> "Perceptual Evaluation of Source Separation for Remixing Music," in 143rd<br> Convention of the Audio Engineering Society, 2017.</p> <p>`fig02/data/` contains the results from single listeners and median results across<br> listeners.<br> `fig02/fig02.plt` is the code to regenerate `fig02/fig02.pdf`.</p> <p>`fig03/data/` contains average medians across all songs.<br> `fig03/fig03.plt` is the code to regenerate `fig03/fig03.pdf`.</p> <p>The listening test results were extracted from the raw data stored together with<br> the experimental procedure in https://doi.org/10.5281/zenodo.835191.<br> There the file `experiment/saves/ratings/analyze_results.py` can be run in order<br> to regenerate the result files provided with this publication.<br> </p>
Music Data, Archiving for Community Use and Future Directions Through the Decade of Indigenous Languages
<p>Music Data, Archiving for Community Use and Future Directions Through the Decade of Indigenous Languages</p> <p>Linda Barwick</p> <p>Presented 5 October 2022 at the international conference "Where Do We Need to Go From Here?" Language Documentation and Archiving in the International Decade of Indigenous Languages</p>
A Stimuli Set of Forty Popular Music Drum Patterns with Perceived Complexity Estimates (Data set)
<p>Stimuli and datasets for the study "A Stimuli Set of Forty Popular Music Drum Patterns with Perceived Complexity Estimates"</p>
Data from: Contrasting nidification behaviors facilitate diversification and colonization of the Music frogs under a changing paleoclimate
<p>In order to cope with the complexity and variability of the terrestrial environment, amphibians have developed a wide range of reproductive and parental behaviors. Nest building occurs in some anuran species as parental care. Species of the Music frog genus <em>Nidirana</em> are known for their unique courtship behavior and mud nesting in several congeners. However, the evolution of these frogs and their nidification behavior has yet to be studied. With phylogenomic and phylogeographic analyses based on a wide sampling of the genus, we find that <em>Nidirana</em> originated from central-southwestern China and the nidification behavior initially evolved at ca 19.3 Ma but subsequently lost in several descendants. Further population genomic analyses suggest that the nidification species have an older diversification and colonization history, while <em>N. adenopleura</em> complex congeners that do not exhibit nidification behavior have experienced a recent rapid radiation. The presence and loss of the nidification behavior in the Music frogs may be associated with paleoclimatic factors such as temperature and precipitation. This study highlights the nidification behavior as a key evolutionary innovation that has contributed to the diversification of an amphibian group under past climate changes.</p>
YouTube Music Artists Data
<p>This dataset was collected using the YouTube Data API v3, covering various artist channels from the YouTube Music app. This dataset provides insight into artists' digital presence and engagement on the YouTube platform, including metrics such as number of subscribers, number of presentations, and engagement rate. Researchers, data analysts, and music fans can use this dataset to explore trends in the music industry, study audience behavior, and analyze the impact of online content on artists' popularity.</p>
music_imagery_data
<p><strong>Data for the study reported in:</strong></p> <ul> <li>Martinez, D. R. Q., Rubio, G. F., Bonetti, L., Achyutuni, K. G., Tzovara, A., Knight, R. T., & Vuust, P. (2024). <br>Decoding reveals the neural representation of perceived and imagined musical sounds (p. 2023.08.15.553456). <br>bioRxiv. https://doi.org/10.1101/2023.08.15.553456</li> </ul> <p><strong>Description</strong></p> <ul> <li><strong>demographics.csv</strong>: Participant demographics.</li> <li><strong>decoding_accuracies</strong>: Time-generalized neural decoding accuracy per participant stored as Python dict containing 2d arrays (training_times x testing_times), with each entry indexing a condition or contrast of interest.</li> <li><strong>decoding_patterns</strong>: Decoding patterns per participant obtained from model coefficients stored as dict containing mne.Epochs arrays indexed by condition or contrast name.</li> <li><strong>epochs</strong>: Per participant single-trial epochs stored as dict containing mne.Epochs arrays after preprocessing (ica removed, high-pass >= 0.05Hz, smoothing with tstep= 25ms and twin=50ms, sfreq=40Hz).</li> <li><strong>figures_data</strong>: Values plotted in each figure and/or the data necessary to obtain them. Excel sheets or csv.</li> <li><strong>inverse_solutions</strong>: Inverse operator per participant to project sensor data into source space (mne inverse class).</li> <li><strong>logs</strong>: Experiment log files. Recall=recognize, manipulation=invert.</li> <li><strong>MNI_transforms</strong>: Transformation matrices per subject to convert source data into common MNI space.</li> <li><strong>neural_accuracy</strong>: Diagonal neural decoding accuracies per subject and time-point, together with behavioral and demographic data.</li> <li><strong>statistics</strong>: Statistical output for the different tests. Dictionaries with entries containing t-statisitcs, p-values, clusters, etc...</li> </ul>
Data from: The music of silence. Part I: Responses to musical imagery encode melodic expectations and acoustics
<p>Musical imagery is the voluntary internal hearing of music in the mind without the need for physical action or external stimulation. Numerous studies have already revealed brain areas activated during imagery. However, it remains unclear to what extent imagined music responses preserve the detailed temporal dynamics of the acoustic stimulus envelope and, crucially, whether melodic expectations play any role in modulating responses to imagined music, as they prominently do during listening. These modulations are important as they reflect aspects of the human musical experience, such as its acquisition, engagement, and enjoyment. This study explored the nature of these modulations in imagined music based on EEG recordings from 21 professional musicians (6 females and 15 males). Regression analyses were conducted to demonstrate that imagined neural signals can be predicted accurately, similarly to the listening task, and were sufficiently robust to allow for accurate identification of the imagined musical piece from the EEG. In doing so, our results indicate that imagery and listening tasks elicited an overlapping but distinctive topography of neural responses to sound acoustics, which is in line with previous fMRI literature. Melodic expectation, however, evoked very similar frontal spatial activation in both conditions, suggesting that they are supported by the same underlying mechanisms. Finally, neural responses induced by imagery exhibited a specific transformation from the listening condition, which primarily included a relative delay and a polarity inversion of the response. This transformation demonstrates the top-down predictive nature of the expectation mechanisms arising during both listening and imagery.</p>
tellingsounds/lama-data: LAMA Data - Capturing Entities (Persons, Topics, Music, etc.) in Austrian audio(-visual) archive material
<p>Data entered into LAMA (Linked Annotations for Media Analysis), a research software for capturing and visualizing the interaction of music and its contexts, developed by the Telling Sounds project.</p>
Raw data and stimuli for assessing the perceived reverberation in different rooms for a set of musical instrument sounds
<p>This set of data and sound stimuli was used in the study by Osses, McLachlan, and Kohlrausch (2020) to assess the perceived reverberation --including measurements and simulations-- for different instrument sounds in eight different rooms. The following are the directories that are provided:</p> <ul> <li><strong>00-Experiment-WAE_GM_201712</strong>: Web Audio Evaluation tool (WAE) used to run the listening experiment with 24 participants. Follow the instructions in README.txt to get the experiment running.</li> <li><strong>01-Stimuli</strong>: Sound stimuli as exactly used during the listening experiments.</li> <li><strong>02-Raw-data</strong> and <strong>03-Results-summary</strong>: Outputs from WAE for each of the participants. The raw data contained in these XML files were extracted and stored in '03-Results-summary'</li> <li><strong>04-Stimuli-9s-for-simulations</strong>: Same sounds as in '01-Stimuli' but truncated to have a duration of 9 s. These sounds were used as input to an implementation (Osses et al. 2017, 2020) of the model by van Dorp et al. (2013).</li> </ul> <p>The paper figures can be reproduced in two MATLAB toolboxes: fastACI (script: publ_osses2020a_JASA_EL_figs.m) and AMT (script: exp_osses2020.m, availability as of 2023).</p>
DATA: Language experience predicts music processing in a half-million speakers of fifty-four languages
<p>This repository contains the data for the paper 'Language experience predicts music processing in a half-million speakers of fifty-four languages'<br> <br> The code that accompanies this data can be found on GitHub - https://github.com/themusiclab/language-experience-music</p>
Data from: Nonlinear decoding models enable music reconstruction from human auditory cortex activity
<p>This dataset is associated with the manuscript "Nonlinear decoding models enable music reconstruction from human auditory cortex activity", and provides all preprocessed files necessary to replicate the results.</p> <p>In this study, we recorded intracranial EEG data (specifically, ECoG) in 29 patients with pharmacoresistant epilepsy while they were passively listening to a Pink Floyd song.</p> <p>The present dataset consists of preprocessed neural activity (High-Frequency Activity, 70-150 Hz), electrode coordinates (in MNI template space) and auditory stimulus (raw wave file, and 32- and 128-frequency-bin auditory spectrogram). HFA and both auditory spectrograms have a sampling rate of 100 Hz, and are temporally aligned (duration of 190.72 s).</p> <p>The code we used to preprocess and analyze the data is hosted on GitHub, <a href="https://github.com/ludovicbellier/PF_HFAdecoding">here</a> for the manuscript and <a href="https://github.com/ludovicbellier/PF_HFAdecoding">there</a> for the predictive modeling functions.</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.