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21 results for “jazz”
Data from: Trade-offs in Coordination Strategies for Duet Jazz Performances Subject to Network Delay and Jitter
<p>This dataset is associated with the paper “Trade-offs in Coordination Strategies for Duet Jazz Performances Subject to Network Delay and Jitter” and includes recordings of improvisations by jazz duos over a network. The related paper is published in <em>Music Perception</em> and is accessible at <a href="https://doi.org/10.1525/mp.2024.42.1.48">doi:10.1525/mp.2024.42.1.48</a> </p> <p><strong>Introduction:</strong></p> <p>This dataset includes data from approximately four hours of live, improvised musical duo performances over a simulated network environment collected in Cambridge, United Kingdom between April-July 2022 as part of a doctoral research project. Data includes audio and video recordings of 130 individual performances, biometric data, and subjective evaluations and comments from the musicians. The primary aim of the project was to collect data via a novel performance capture and manipulation system for use in the empirical modelling of ensemble coordination strategies during networked music-making. This analysis is reported in Cheston, Cross, and Harrison (2023), "Trade-offs in Coordination Strategies for Networked Jazz Performances". Please refer to this publication for full details on the data collection procedure. Our codebook is <a href="https://github.com/HuwCheston/Jazz-Jitter-Analysis">hosted on GitHub</a> and, in conjunction with this dataset, can be used to reproduce the analysis contained in the article.</p> <p>The ten musicians shown in these recordings were recruited for their expertise in jazz improvisation. They were grouped into five duos consisting each of one pianist and drummer, with no musician performing in more than one duo. Participants were instructed to improvise together over a standard twelve-bar blues musical structure, but following a formula which required them to provide a clear and unambiguous pulse of continuous quarter notes. Varying amounts of network latency and jitter were simulated for each performance, consisting respectively of the minimum amount of delay applied to the live feedback a musician heard from their partner and the degree that this delay varied. The amount of latency and jitter applied to the performance is summarised in the file or directory name for each performance and is described in detail in the above publication. Note that latency and jitter conditions were presented in a random order for each duo.</p> <p><strong>Data collected includes:</strong></p> <ul> <li>audio recordings for each performance, with and without delay, collected via direct line-in (MIDI, WAV).</li> <li>video recordings, collected via high-quality webcams (MKV, AVI).</li> <li>streams of the quarter note pulse provided by each musician in a performance (MIDI).</li> <li>muxed audio-visual recordings of both participants in each performance (MP4)</li> <li>accelerometer and photoplethysmography streams, collected from arm-worn devices (TXT, duos 3-5 only)</li> <li>questionnaire responses from performers, evaluating each condition (XLSX)</li> <li>ratings of performance quality from an unbiased sample of listeners, collected during an online perceptual study (CSV)</li> </ul> <p><strong>Repository structure:</strong></p> <p><strong><em>NB: please see <a href="https://huwcheston.github.io/Jazz-Jitter-Analysis/getting-started.html">this section of the code documentation website</a> for a full description of how to recreate the analyses and models created in the paper.</em></strong></p> <p>The files <em>data.zip </em>and <em>data.z0*</em> contain all data collected from the study, APART from the perceptual study stimuli & results. To open these files, download the <em>data.zip</em> file and <em><strong>all the corresponding volumes ending in .z0 </strong></em>and open the <em>data.zip</em> file using a tool for opening multi-part zip files, such as WinRAR. <em>Do not try to open the files ending in .z0</em>, otherwise you may get a message about the data being corrupted. Inside <em>data.zip</em>, you'll see the following folders and files:</p> <ul> <li><em>avmanip_output</em>: the raw MIDI, audio, and video output from each performance <ul> <li>the subfolders are organised with a single folder per participant duo, experimental block, and condition.</li> <li>avmanip_output\trial_1\Block 1\Condition 1 - 23 05 relates to the performance of the first duo of participants in the first session of the experiment, in the first condition they encountered, with 23ms of latency and 0.5x jitter.</li> </ul> </li> <li><em>midi_bpm_cleaning</em>: the cleaned MIDI files (quarter note onset positions) <ul> <li>the subfolders are organised similarly to the <em>avmanip_output</em> folder, using the same conventions.</li> </ul> </li> <li><em>muxed_performances</em>: the combined audio-video .mp4 files from each performance <ul> <li>these files are labelled in the format: duo_session_latency_jitter_keysfmt_drumsfmt.</li> <li>muxed_performances\kdelay_ddelay\d1_s1_l23_j00_kdelay_ddelay.mp4 relates to the performance of the first duo of participants in the first session of the experiment, in the first condition they encountered, with 23ms of latency and 0.5x jitter, and with latency and jitter applied to both keys and drummer.</li> <li>for more information on recreating these videos, <a href="https://huwcheston.github.io/Jazz-Jitter-Analysis/getting-started.html#reproduce-combined-audio-visual-stimuli">see the linked section of the code documentation website.</a></li> </ul> </li> <li><em>questionnaire_anonymized</em>: the anonymized questionnaire responses given by participants, also contained in the supplementary material of the associated paper (see preprint).</li> </ul> <p>Alongside <em>data.zip </em>and the <em>data.z0*</em> archives, there are two further loose files, <em>Database View Participant - Dashboard.csv, Database View SuccessTrial - Dashboard.csv, </em>which are the anonymized demographic and response data from the perceptual experiment, and one loose archive folder <em>perceptual_study_videos.rar</em>, which contains the stimuli used in the perceptual experiment.</p> <p>To reproduce the analysis from the paper, all files should be unzipped into the \data\raw directory of the code repository created after <a href="https://github.com/HuwCheston/Jazz-Jitter-Analysis">cloning this from GitHub</a>. For more detail and instructions on installation, <a href="https://huwcheston.github.io/Jazz-Jitter-Analysis/getting-started.html">see the section of the code documentation website linked here</a>.</p> <p><strong>Usage:</strong></p> <p>These recordings of live, improvised duo performances are unattributed and anonymised as agreed with participants at the point of data collection. The musicians involved received a one-off, fixed payment for their time and had their travel expenses reimbursed, with funding provided by Cambridge Digital Humanities (<a href="https://www.cdh.cam.ac.uk/research/projects/newmusicsoftwareplatform/">project page</a>). All participants consented to the use of their recordings for projects by the current authors and for these recordings to be shared with interested members of the music psychology community, with the intention of furthering academic research. The musicians did not intend that the recordings be used for commercial, artistic, or entertainment purposes, and such use is not permitted.</p> <p><strong>Citation:</strong></p> <p>If you use this dataset in your research, please cite the paper it relates to:</p> <pre><code>@article{10.1525/mp.2024.42.1.48, author = {Cheston, Huw and Cross, Ian and Harrison, Peter M. C.}, title = "{Trade-offs in Coordination Strategies for Duet Jazz Performances Subject to Network Delay and Jitter}", journal = {Music Perception}, volume = {42}, number = {1}, pages = {48-72}, year = {2024}, month = {09}, issn = {0730-7829}, doi = {10.1525/mp.2024.42.1.48}, url = {https://doi.org/10.1525/mp.2024.42.1.48}, eprint = {https://online.ucpress.edu/mp/article-pdf/42/1/48/833292/mp.2024.42.1.48.pdf}, }</code></pre> <p><strong>Contact:</strong></p> <p>Huw Cheston - <a href="http://twitter.com/huwcheston/">@huwcheston</a> - hwc31@cam.ac.uk</p>
FiloBass: A Dataset and Corpus Based Study of Jazz Basslines
<p>Dataset to accompany the paper "FiloBass: A Dataset and Corpus Based Study of Jazz Basslines" which was published at ISMIR 2023.</p>
A Small Dataset of Jazz Guitar Licks for Automatic Transcription Experiments
<p>A dataset consisting of ten (mostly monophonic) jazz guitar licks that were performed by François Pachet and recorded at the Sony Computer Science Laboratory (CSL) in Paris.</p>
Two Dynamic Attributed Networks: Enron & Jazz LastFM
<p><strong>Description. </strong>This repository contains two dynamic and attributed social networks extracted from the well-known Enron email dataset, and from the LastFM online music platform. We used both networks in the following papers:</p> <ol> <li>G. K. Orman, V. Labatut, M. Plantevit, and J.-F. Boulicaut, “A Method for Characterizing Communities in Dynamic Attributed Complex Networks,” in <em>IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM)</em>, 2014, pp. 481–484. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01011913">hal-01011913</a>⟩ DOI: <a href="http://doi.org/10.1109/ASONAM.2014.6921629">10.1109/ASONAM.2014.6921629</a></li> <li>G. K. Orman, V. Labatut, M. Plantevit, and J.-F. Boulicaut, “Interpreting communities based on the evolution of a dynamic attributed network,” <em>Social Network Analysis and Mining</em>, vol. 5, p. 20, 2015. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01163778">hal-01163778</a>⟩ DOI: <a href="http://doi.org/10.1007/s13278-015-0262-4">10.1007/s13278-015-0262-4</a></li> </ol> <p><strong>Citation. </strong>If you use these data, please cite the paper [1].</p> <p><br><code>@InProceedings{Orman2014,</code><br><code> author = {Orman, Günce Keziban and Labatut, Vincent and Plantevit, Marc and Boulicaut, Jean-François},</code><br><code> title = {A Method for Characterizing Communities in Dynamic Attributed Complex Networks},</code><br><code> booktitle = {IEEE/ACM International Conference on Advances in Social Network Analysis and Mining},</code><br><code> year = {2014},</code><br><code> pages = {481-484},</code><br><code> address = {Beijing, CN},</code><br><code> publisher = {IEEE Publishing},</code><br><code> doi = {10.1109/ASONAM.2014.6921629},</code><br><code>}</code></p> <p>----------------------------------------</p> <p><strong>Enron dataset. </strong>Enron is a well-known dataset in network science and text mining. It has been widely studied in academia. In network science, several different static networks appear in the literature. However, up to now, no dynamic network has been published, even though the email conversations have timestamps.</p> <p>We processed the original dataset to extract a dynamic network. There are 158 nodes representing Enron employees between 1997 and 2002. All the addresses in the <em>From</em> and <em>To</em> fields of each email are considered, resulting in a network of 28,802 nodes representing a distinct email addresses. A time span of one month is chosen for the time slices, generating 46 time slices. Two nodes are connected if the corresponding persons emailed each other during the given time slice. We did not make any distinction between sender and receiver, and thus produced an undirected dynamic network. </p> <p>----------------------------------------</p> <p><strong>LastFM dataset. </strong>LastFM is a music website that allows its members to register and listen to music online. It is also a social network platform, because its members can declare friendship relationships. In LastFM, members can join a predefined group related to their music tastes, and participate in music-related events such as concerts. Using the LastFM API, One can retrieve the information of the artist and track a user has listened to, with the exact timestamp. Moreover, it is also possible to get some information regarding the music-related events the users joined, including the exact timestamps.</p> <p>We extracted a network by focusing on the members of the <em>Jazz</em> group, which is supposed to include users appreciating this type of music. We took advantage of the LastFM API to retrieve the members of this group and the existing friendship connection between them. In the end, our network contains 1,702 nodes representing the <em>Jazz</em> users. The friendship relationships between them is static, though, in the sense that the LastFM API does not give access to any temporal information regarding their beginning or end. So, we decided to take advantage of some additional information to get a dynamic structure. We put a link between two nodes if two conditions were simultaneously true: 1) both considered users listened to at least one common artist for a specific period of time, and 2) they are friends on the LastFM platform. For the mentioned period of time, we decided to use 3 months with 1 month overlap, after having analyzed the dynamics of the platform. In other words, we extracted a dynamic network in which each time slice represents three months of LastFM usage for our 1,702 users of interest. There are one month overlap between two consecutive time slices.</p>
JAZZVAR: A Dataset of Variations found within Solo Piano Performances of Jazz Standards for Music Overpainting
<p>Release of the MIDI data pairs that constitute the JAZZVAR dataset. See below for the abstract of the publication.</p> <p>The data is also available transposed to C/Am and subsequently, to all keys, with accompanying metadata.</p> <p>Abstract:</p> <p>Jazz pianists often uniquely interpret jazz standards. Passages from these interpretations can be viewed as sections of variation. We manually extracted such variations from solo jazz piano performances. The JAZZVAR dataset is a collection of 502 pairs of Variation and Original MIDI segments. Each Variation in the dataset is accompanied by a corresponding Original segment containing the melody and chords from the original jazz standard. Our approach differs from many existing jazz datasets in the music information retrieval (MIR) community, which often focus on improvisation sections within jazz performances. In this paper, we outline the curation process for obtaining and sorting the repertoire, the pipeline for creating the Original and Variation pairs, and our analysis of the dataset. We also introduce a new generative music task, Music Overpainting, and present a baseline Transformer model trained on the JAZZVAR dataset for this task. Other potential applications of our dataset include expressive performance analysis and performer identification.</p>
Dataset: Jazz Pharmaceuticals plc (JAZZ) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
JAAH: Audio-aligned jazz harmony dataset
<p>The dataset consists of annotations of 113 tracks selected from “The Smithsonian Collection of Classic Jazz” and “Jazz: The Smithsonian Anthology,” covering a range of performers, subgenres, and historical periods. Annotations were made by a jazz musician and contain information about the meter, structure, and chords for entire audio tracks.</p> <p>For referencing the JAAH, and obtaining a more detailed description of it, please refer to:</p> <blockquote>Eremenko, V., Demirel, E., Bozkurt, B., & Serra, X. (2018). <a href="https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&ved=2ahUKEwiU2cGnvoLgAhUkiKYKHT96AW0QFjACegQICBAC&url=https%3A%2F%2Fzenodo.org%2Frecord%2F1291834%2Ffiles%2FJazzHarmonyDataset_Ismir2018.pdf&usg=AOvVaw38mi5jVoPpKLWiIJYt5CGl">Audio-aligned jazz harmony dataset for automatic chord transcription and corpus-based research.</a> International Society for Music Information Retrieval Conference.</blockquote>
iRealPro Corpus of Jazz Standards
<p>The first release of 1,186 kern files from the iRealB Corpus of Jazz Standards. A discussion of the conversion and basic descriptive statistics can be found here:</p> <p><a href="http://icmpc-escom2012.web.auth.gr/files/papers/909_Proc.pdf">http://icmpc-escom2012.web.auth.gr/files/papers/909_Proc.pdf</a></p>
PiJAMA: Piano Jazz with Automatic MIDI Annotations
<p>Release of the automatic MIDI transcriptions that constitute the PiJAMA dataset. See below for the abstract of the publication.<br> <br> Abstract:</p> <p>Recent advances in automatic piano transcription have enabled large scale analysis of piano music in the symbolic domain. However, the research has largely focused on classical piano music. We present <strong>PiJAMA</strong> (<strong>Pi</strong>ano <strong>J</strong>azz with <strong>A</strong>utomatic <strong>M</strong>IDI <strong>A</strong>nnotations): a dataset of over 200 hours of solo jazz piano performances with automatically transcribed MIDI. In total there are 2,777 unique performances by 120 different pianists across 244 recorded albums. The dataset contains a mixture of studio recordings and live performances. We use automatic audio tagging to identify applause, spoken introductions, and other non-piano audio to facilitate downstream music information retrieval tasks. We explore descriptive statistics of the MIDI data, including pitch histograms and chromaticism. We then demonstrate two experimental benchmarks on the data: performer identification and generative modeling. The dataset, including a link to the associated source code is available at <a href="https://almostimplemented.github.io/PiJAMA/">https://almostimplemented.github.io/PiJAMA/</a>.</p>
Do you like Jazz?
I hate it. *screams and drops plates* Source: Objaverse 1.0 / Sketchfab
Jazz Muscians (VCU_3D_7227)
This relief sculpture is part of an exhibit on Harlem at the New York State Museum. It was captured with an iPad Pro 2 and the Scaniverse app on 27 May 2022. Source: Objaverse 1.0 / Sketchfab
Instrumented Thoracic and Lumbar Arthrodesis Supplemented by the Implanet Jazz SystemTMd
ClinicalTrials.gov study NCT02411799. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Byjacco Dolores-Jazz Three Step
<p>Byjacco Dolores-Jazz Three Step</p>
King of Jazz SelectionAC
<p>King of Jazz SelectionAC</p>
Data from: Trade-offs in Coordination Strategies for Networked Jazz Performances (anonymized for peer review)
<p>This dataset is associated with the paper “Trade-offs in Coordination Strategies for Networked Jazz Performances” and includes recordings of improvisations by jazz duos over a network.</p> <p><strong>Introduction:</strong></p> <p>This dataset includes data from approximately four hours of live, improvised musical duo performances over a simulated network environment collected in LOCATION REMOVED FOR PEER REVIEW. Data includes audio and video recordings of 130 individual performances, biometric data, and subjective evaluations and comments from the musicians. The primary aim of the project was to collect data via a novel performance capture and manipulation system for use in the empirical modelling of ensemble coordination strategies during networked music-making. This analysis is reported in ANONYMIZED FOR PEER REVIEW. Please refer to this publication for full details on the data collection procedure.</p> <p>The ten musicians shown in these recordings were recruited for their expertise in jazz improvisation. They were grouped into five duos consisting each of one pianist and drummer, with no musician performing in more than one duo. Participants were instructed to improvise together over a standard twelve-bar blues musical structure, but following a formula which required them to provide a clear and unambiguous pulse of continuous quarter notes. Varying amounts of network latency and jitter were simulated for each performance, consisting respectively of the minimum amount of delay applied to the live feedback a musician heard from their partner and the degree that this delay varied. The amount of latency and jitter applied to the performance is summarised in the file or directory name for each performance and is described in detail in the above publication. Note that latency and jitter conditions were presented in a random order for each duo.</p> <p><strong>Data collected includes:</strong></p> <ul> <li>audio recordings for each performance, with and without delay, collected via direct line-in (MIDI, WAV).</li> <li>video recordings, collected via high-quality webcams (MKV, AVI).</li> <li>streams of the quarter note pulse provided by each musician in a performance (MIDI).</li> <li>muxed audio-visual recordings of both participants in each performance (MP4)</li> <li>accelerometer and photoplethysmography streams, collected from arm-worn devices (TXT, duos 3-5 only)</li> <li>questionnaire responses from performers, evaluating each condition (XLSX)</li> <li>ratings of performance quality from an unbiased sample of listeners, collected during an online perceptual study (CSV)</li> </ul> <p><strong>Repository structure:</strong></p> <p><strong><em>NB: please see this section of the code documentation website (LINK REMOVED FOR PEER REVIEW) for a full description of how to recreate the analyses and models created in the paper.</em></strong></p> <p>The files <em>data.zip </em>and <em>data.z0*</em> contain all data collected from the study, APART from the perceptual study stimuli & results. To open these files, download the <em>data.zip</em> file and <em><strong>all the corresponding volumes ending in .z0 </strong></em>and open the <em>data.zip</em> file using a tool for opening multi-part zip files, such as WinRAR. <em>Do not try to open the files ending in .z0</em>, otherwise you may get a message about the data being corrupted. Inside <em>data.zip</em>, you'll see the following folders and files:</p> <ul> <li><em>avmanip_output</em>: the raw MIDI, audio, and video output from each performance <ul> <li>the subfolders are organised with a single folder per participant duo, experimental block, and condition.</li> <li>avmanip_output\trial_1\Block 1\Condition 1 - 23 05 relates to the performance of the first duo of participants in the first session of the experiment, in the first condition they encountered, with 23ms of latency and 0.5x jitter.</li> </ul> </li> <li><em>midi_bpm_cleaning</em>: the cleaned MIDI files (quarter note onset positions) <ul> <li>the subfolders are organised similarly to the <em>avmanip_output</em> folder, using the same conventions.</li> </ul> </li> <li><em>muxed_performances</em>: the combined audio-video .mp4 files from each performance <ul> <li>these files are labelled in the format: duo_session_latency_jitter_keysfmt_drumsfmt.</li> <li>muxed_performances\kdelay_ddelay\d1_s1_l23_j00_kdelay_ddelay.mp4 relates to the performance of the first duo of participants in the first session of the experiment, in the first condition they encountered, with 23ms of latency and 0.5x jitter, and with latency and jitter applied to both keys and drummer.</li> <li>for more information on recreating these videos, see the linked section of the code documentation website (LINK REMOVED FOR PEER REVIEW).</li> </ul> </li> <li><em>questionnaire_anonymized</em>: the anonymized questionnaire responses given by participants, also contained in the supplementary material of the associated paper (see preprint).</li> </ul> <p>Alongside <em>data.zip </em>and the <em>data.z0*</em> archives, there are two further loose files, <em>Database View Participant - Dashboard.csv, Database View SuccessTrial - Dashboard.csv, </em>which are the anonymized demographic and response data from the perceptual experiment, and one loose archive folder <em>perceptual_study_videos.rar</em>, which contains the stimuli used in the perceptual experiment.</p> <p><strong>Usage:</strong></p> <p>To reproduce the analysis, models, and graphs from the paper, download the code in code-analysis-modeling.rar and extract it to a new folder, then extract all the files inside <em>data.zip</em> and the two perceptual study CSV files into \data\raw. Install Python 3.10 if you don't have it already, and then open a command prompt in the root directory of the analysis code and execute the command <em>run.cmd</em><em>.</em></p> <p>The remaining code files (<em>code-perceptual-study.rar</em> and <em>code-testbed-software.rar</em>) contain the code used in the perceptual and laboratory experiments. Documentation and installation instructions are provided within each archive.</p> <p>These recordings of live, improvised duo performances are unattributed and anonymized as agreed with participants at the point of data collection. The musicians involved received a one-off, fixed payment for their time and had their travel expenses reimbursed, with funding provided by ANONYMIZED FOR PEER REVIEW. All participants consented to the use of their recordings for projects by the current authors and for these recordings to be shared with interested members of the music psychology community, with the intention of furthering academic research. The musicians did not intend that the recordings be used for commercial, artistic, or entertainment purposes, and such use is not permitted.</p> <p><strong>Citation:</strong></p> <p>If you use this dataset in your research, please follow the citation format posted on GitHub (LINK REMOVED FOR PEER REVIEW).</p> <p><strong>Contact:</strong></p> <p>ANONYMIZED FOR PEER REVIEW</p>
The Effect of Jazz on Postoperative Pain and Stress in Patients Undergoing Elective Hysterectomy
ClinicalTrials.gov study NCT01834027. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Audio Data from: Jazz Trio Database
<p>The <strong>Jazz Trio Database</strong> is a dataset composed of about 45 hours of improvised jazz performances annotated by an automated signal processing pipeline. See our publication in <em>Transactions of the International Society for Music Information Retrieval </em>(<a href="https://doi.org/10.5334/tismir.186" target="_blank" rel="noopener noreferrer">DOI: 10.5334/tismir.186</a>).</p> <p>This repository contains the <strong>audio files</strong> used to create the annotations (both mixed, "raw" files, and unmixed, "stem" files processed using <em>audio source separation</em> models). Its purpose is to serve as a reference database for the design, evaluation, and implementation of various music information retrieval systems, including (but not limited to) onset detection, beat tracking, automatic music transcription, and automatic performer identification.</p> <p>No annotations or metadata are provided with this archive; these can instead be found on the <a href="https://github.com/HuwCheston/Jazz-Trio-Database/releases">GitHub repository</a>. Track names are consistent between audio, metadata, and annotations for a single track. All files are encoded as stereo 16-bit .WAV audio with a sample rate of 44.1 kHz.</p> <p><strong>Downloading:</strong></p> <p>We provide audio for <em>JTD </em>as two multi-part .zip files, one containing the unmixed audio and another containing the separated stems. After you've been granted access to the Zenodo record, the simplest way to download the entire database is to press the "Download all" button.</p> <p> </p> <p>This will create a new file named <code>files-archive</code> (with no extension). Rename the file to <code>files-archive.zip</code> and extract using any unzipping tool (7zip, WinRAR, the unarchiver) or the command line. This will give you a list of multi-part zip files in the form <code>[processed.zip.001, processed.zip.002, ...]</code> and <code>[raw.zip.001, raw.zip.002, ...]</code>. </p> <p>To extract these, use 7zip from the command line with the following code:</p> <p><code><span>7z x processed.<span>zip</span><span>.001</span></span></code></p> <p><code><span><span>7z x raw.zip.001</span></span></code></p> <p>Note that the default `unzip` command on Linux can't handle these files, so you'll need to use 7zip. You may also be able to use a GUI tool like WinRAR, which was used to create the archive in the first place. Also, be aware that each of those commands will extract all the wavs to the current folder, so you’ll likely need to move them afterwards.</p> <p><em>Thanks to Xavier Riley for providing these instructions!</em></p> <p><strong>License</strong></p> <p><em>JTD</em> audio is provided for <em>academic research purposes only</em> and the material contained within it should not be used for any commercial purpose without the express permission of the copyright holders. Access will only be granted for research projects, and potential users of the audio data must apply for access to the data. These requests are checked manually: please do not fill in the form multiple times, we will aim to grant you access as soon as possible.</p> <p>Note that the annotations and metadata are provided on an open access basis and do not require permission to be granted. </p> <p><strong>Citation</strong></p> <p>If you use the Jazz Trio Database in your work, please cite the paper where it was first introduced:</p> <pre><code>@article{jazz-trio-database title = {Jazz Trio Database: Automated Annotation of Jazz Piano Trio Recordings Processed Using Audio Source Separation}, url = {https://doi.org/10.5334/tismir.186}, doi = {10.5334/tismir.186}, publisher = {Transactions of the International Society for Music Information Retrieval}, author = {Cheston, Huw and Schlichting, Joshua L and Cross, Ian and Harrison, Peter M C}, year = {2024}, }</code></pre>
An HMM-based approach for Cross-Harmonization of Jazz Standards - results
<p>Videos and logs of the results presented in: "An HMM-based approach for Cross-Harmonization of Jazz Standards".</p>
Thoracic-Lumbar Arthrodesis- Implanet Jazz
ClinicalTrials.gov study NCT05366140. IPD Sharing: NO. Countries: 1. Publications: 0.
Jazz Music and Mindfulness for Chronic Pain
ClinicalTrials.gov study NCT05979012. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
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.