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28 results for “recorded 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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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>
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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GUITAR-FX-DIST: A Dataset of Processed Guitar Recordings for Music Research - (Poly Continuous)
<p><strong>GUITAR-FX-DIST</strong> is a dataset of electric guitar recordings processed with overdrive, distortion and fuzz audio effects. It was developed for research in guitar effects detection, classification and parameters estimation. The dataset is also useful for research on automatic music transcription, intelligent music production, signal processing or effects modelling. It contains both unprocessed and processed recordings.</p> <p>The dataset is split into 4 sub-datasets: Mono Continuous, Mono Discrete, Poly Continuous, Poly Discrete</p> <p> </p> <p><strong>Authors:</strong></p> <p>Marco Comunità - <a href="http://c4dm.eecs.qmul.ac.uk/">Centre for Digital Music</a>, Queen Mary University of London</p> <p> </p> <p><strong>Reference:</strong></p> <p>If you make use of GUITAR-FX-DIST, please cite the following publication:</p> <pre><code>@article{comunità2021guitar, title={Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks}, author={Comunità, Marco and Stowell, Dan and Reiss, Joshua D.}, journal={Journal of the Audio Engineering Society}, year={2021}, volume={69}, number={7/8}, pages={594-604}, doi={}, month={July} }</code></pre> <p> </p> <p><strong>Dataset Snapshot:</strong></p> <ul> <li><strong>Size:</strong> ~550k samples (~305 hours) + 550k mel spectrograms</li> <li><strong>Audio Format:</strong> WAV - 44.1kHz, 16bit, mono, -6dBFS</li> <li><strong>Mel-Spectrogram Format:</strong> NPY - 128 frequency bands, sample rate 22050Hz, window length 1024, hop size 512,</li> <li><strong>Effects:</strong> 14 between overdrive, distortion and fuzz</li> <li><strong>Unprocessed recordings</strong> <ul> <li>624 monophonic notes</li> <li>420 polyphonic (2, 3 and 4 notes intervals and chords)</li> <li>2 guitars, with up to 2 pick-up settings and up to 3 plucking styles (finger pluck - hard, finger pluck - soft, pick) <ul> <li>Schecter Diamond C-1 Classic</li> <li>Chester Stratocaster</li> </ul> </li> </ul> </li> <li><strong>Samples length:</strong> 2 sec</li> </ul> <p> </p> <p><strong>Unprocessed Recordings:</strong></p> <p>The original (unprocessed) recordings are from the <a href="https://www.idmt.fraunhofer.de/en/business_units/m2d/smt/audio_effects.html">IDMT-SMT-Audio-Effects</a> dataset.</p> <p>For details please refer to the website and the accompagning publication:</p> <p><em>Stein, Michael; Abeßer, Jakob; Dittmar, Christian; Schuller, Gerald: Automatic Detection of Audio Effects in Guitar and Bass Recordings. Proceedings of the AES 128th Convention, 2010.</em></p> <p> </p> <p><strong>Processed Recordings:</strong></p> <p>The processed recordings are divided into 4 sub-datasets which are named depending on the unprocessed recordings used (monophonic or polyphonic) and on the settings' values (discrete or continuous).</p> <p>The sub-datasets are called: Mono Discrete, Poly Discrete, Mono Continuous, Poly Continuous</p> <p>Mono Discrete and Poly Discrete use a discrete set of combinations selected as the most common and representative settings a person might use (see README file for details).</p> <p>For Mono Continuous and Poly Continuous both unprocessed samples as well as settings’ values are drawn from a uniform distribution (10000 samples for each effect).</p> <p>Samples:</p> <ul> <li>Mono Discrete: ~160k</li> <li>Poly Discrete: ~110k</li> <li>Mono Continuous: 140k</li> <li>Poly Continuous: 140k</li> </ul> <p> </p> <p><strong>Scripts:</strong></p> <p>The dataset includes the MATLAB scripts used to generate the samples</p>
GUITAR-FX-DIST: A Dataset of Processed Guitar Recordings for Music Research - (Mono Discrete)
<p><strong>GUITAR-FX-DIST</strong> is a dataset of electric guitar recordings processed with overdrive, distortion and fuzz audio effects. It was developed for research in guitar effects detection, classification and parameters estimation. The dataset is also useful for research on automatic music transcription, intelligent music production, signal processing or effects modelling. It contains both unprocessed and processed recordings.</p> <p>The dataset is split into 4 sub-datasets: Mono Continuous, Mono Discrete, Poly Continuous, Poly Discrete</p> <p> </p> <p><strong>Authors:</strong></p> <p>Marco Comunità - <a href="http://c4dm.eecs.qmul.ac.uk/">Centre for Digital Music</a>, Queen Mary University of London</p> <p> </p> <p><strong>Reference:</strong></p> <p>If you make use of GUITAR-FX-DIST, please cite the following publication:</p> <pre><code>@article{comunità2021guitar, title={Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks}, author={Comunità, Marco and Stowell, Dan and Reiss, Joshua D.}, journal={Journal of the Audio Engineering Society}, year={2021}, volume={69}, number={7/8}, pages={594-604}, doi={}, month={July} }</code></pre> <p> </p> <p><strong>Dataset Snapshot:</strong></p> <ul> <li><strong>Size:</strong> ~550k samples (~305 hours) + 550k mel spectrograms</li> <li><strong>Audio Format:</strong> WAV - 44.1kHz, 16bit, mono, -6dBFS</li> <li><strong>Mel-Spectrogram Format:</strong> NPY - 128 frequency bands, sample rate 22050Hz, window length 1024, hop size 512,</li> <li><strong>Effects:</strong> 14 between overdrive, distortion and fuzz</li> <li><strong>Unprocessed recordings</strong> <ul> <li>624 monophonic notes</li> <li>420 polyphonic (2, 3 and 4 notes intervals and chords)</li> <li>2 guitars, with up to 2 pick-up settings and up to 3 plucking styles (finger pluck - hard, finger pluck - soft, pick) <ul> <li>Schecter Diamond C-1 Classic</li> <li>Chester Stratocaster</li> </ul> </li> </ul> </li> <li><strong>Samples length:</strong> 2 sec</li> </ul> <p> </p> <p><strong>Unprocessed Recordings:</strong></p> <p>The original (unprocessed) recordings are from the <a href="https://www.idmt.fraunhofer.de/en/business_units/m2d/smt/audio_effects.html">IDMT-SMT-Audio-Effects</a> dataset.</p> <p>For details please refer to the website and the accompagning publication:</p> <p><em>Stein, Michael; Abeßer, Jakob; Dittmar, Christian; Schuller, Gerald: Automatic Detection of Audio Effects in Guitar and Bass Recordings. Proceedings of the AES 128th Convention, 2010.</em></p> <p> </p> <p><strong>Processed Recordings:</strong></p> <p>The processed recordings are divided into 4 sub-datasets which are named depending on the unprocessed recordings used (monophonic or polyphonic) and on the settings' values (discrete or continuous).</p> <p>The sub-datasets are called: Mono Discrete, Poly Discrete, Mono Continuous, Poly Continuous</p> <p>Mono Discrete and Poly Discrete use a discrete set of combinations selected as the most common and representative settings a person might use (see README file for details).</p> <p>For Mono Continuous and Poly Continuous both unprocessed samples as well as settings’ values are drawn from a uniform distribution (10000 samples for each effect).</p> <p>Samples:</p> <ul> <li>Mono Discrete: ~160k</li> <li>Poly Discrete: ~110k</li> <li>Mono Continuous: 140k</li> <li>Poly Continuous: 140k</li> </ul> <p> </p> <p><strong>Scripts:</strong></p> <p>The dataset includes the MATLAB scripts used to generate the samples</p>
GUITAR-FX-DIST: A Dataset of Processed Guitar Recordings for Music Research - (Poly Discrete)
<p><strong>GUITAR-FX-DIST</strong> is a dataset of electric guitar recordings processed with overdrive, distortion and fuzz audio effects. It was developed for research in guitar effects detection, classification and parameters estimation. The dataset is also useful for research on automatic music transcription, intelligent music production, signal processing or effects modelling. It contains both unprocessed and processed recordings.</p> <p>The dataset is split into 4 sub-datasets: Mono Continuous, Mono Discrete, Poly Continuous, Poly Discrete</p> <p> </p> <p><strong>Authors:</strong></p> <p>Marco Comunità - <a href="http://c4dm.eecs.qmul.ac.uk/">Centre for Digital Music</a>, Queen Mary University of London</p> <p> </p> <p><strong>Reference:</strong></p> <p>If you make use of GUITAR-FX-DIST, please cite the following publication:</p> <pre><code>@article{comunità2021guitar, title={Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks}, author={Comunità, Marco and Stowell, Dan and Reiss, Joshua D.}, journal={Journal of the Audio Engineering Society}, year={2021}, volume={69}, number={7/8}, pages={594-604}, doi={}, month={July} }</code></pre> <p> </p> <p><strong>Dataset Snapshot:</strong></p> <ul> <li><strong>Size:</strong> ~550k samples (~305 hours) + 550k mel spectrograms</li> <li><strong>Audio Format:</strong> WAV - 44.1kHz, 16bit, mono, -6dBFS</li> <li><strong>Mel-Spectrogram Format:</strong> NPY - 128 frequency bands, sample rate 22050Hz, window length 1024, hop size 512,</li> <li><strong>Effects:</strong> 14 between overdrive, distortion and fuzz</li> <li><strong>Unprocessed recordings</strong> <ul> <li>624 monophonic notes</li> <li>420 polyphonic (2, 3 and 4 notes intervals and chords)</li> <li>2 guitars, with up to 2 pick-up settings and up to 3 plucking styles (finger pluck - hard, finger pluck - soft, pick) <ul> <li>Schecter Diamond C-1 Classic</li> <li>Chester Stratocaster</li> </ul> </li> </ul> </li> <li><strong>Samples length:</strong> 2 sec</li> </ul> <p> </p> <p><strong>Unprocessed Recordings:</strong></p> <p>The original (unprocessed) recordings are from the <a href="https://www.idmt.fraunhofer.de/en/business_units/m2d/smt/audio_effects.html">IDMT-SMT-Audio-Effects</a> dataset.</p> <p>For details please refer to the website and the accompagning publication:</p> <p><em>Stein, Michael; Abeßer, Jakob; Dittmar, Christian; Schuller, Gerald: Automatic Detection of Audio Effects in Guitar and Bass Recordings. Proceedings of the AES 128th Convention, 2010.</em></p> <p> </p> <p><strong>Processed Recordings:</strong></p> <p>The processed recordings are divided into 4 sub-datasets which are named depending on the unprocessed recordings used (monophonic or polyphonic) and on the settings' values (discrete or continuous).</p> <p>The sub-datasets are called: Mono Discrete, Poly Discrete, Mono Continuous, Poly Continuous</p> <p>Mono Discrete and Poly Discrete use a discrete set of combinations selected as the most common and representative settings a person might use (see README file for details).</p> <p>For Mono Continuous and Poly Continuous both unprocessed samples as well as settings’ values are drawn from a uniform distribution (10000 samples for each effect).</p> <p>Samples:</p> <ul> <li>Mono Discrete: ~160k</li> <li>Poly Discrete: ~110k</li> <li>Mono Continuous: 140k</li> <li>Poly Continuous: 140k</li> </ul> <p> </p> <p><strong>Scripts:</strong></p> <p>The dataset includes the MATLAB scripts used to generate the samples</p>
Recordings for loudness analysis of the music mixes for comparison of wave field synthesis, surround, and stereo
<p>Mat files of live recordings of the music mixes for the comparison of wave field synthesis, surround, and stereo listening test. The recordings were performed at different levels and were analyzed with a loudness model afterwards for adjusting the levels. For details, see</p> <p>C. Hold, H. Wierstorf, A. Raake, The Difference Between Stereophony and Wave Field Synthesis in the Context of Popular Music, in 140th AES Convention, 2016.</p>
Recording and analysing physical control variables used in clarinet playing: A Musical Instrument Performance Capture and Analysis Toolbox (MIPCAT)
<p>Measuring fine-grained physical interaction between the human player and the musical instrument can significantly improve our understanding of music performance. This article presents a Musical Instrument Performance Capture and Analysis Toolbox (MIPCAT) that can be used to capture and to process the physical control variables used by a clarinettist while performing music. This includes both a measurement apparatus with sensors and a software toolbox for analysis. Several of the components used here can also be applied in other musical contexts. Applied to the clarinet, the instrument sensors record blowing pressure, reed position, tongue contact and sound pressures in the mouth, mouthpiece and barrel. Radiated sound and multiple videos are also recorded to allow details of the embouchure and the instrument’s motion to be determined. The software toolbox can synchronise measurements from different devices, extract time-variable descriptors, segment by notes and excerpts, and summarise descriptors per note, phrase or excerpt. An example of its application is given showing how to compare performances from different musicians.Measuring fine-grained physical interaction between the human player and the musical instrument can significantly improve our understanding of music performance. This article presents a Musical Instrument Performance Capture and Analysis Toolbox (MIPCAT) that can be used to capture and to process the physical control variables used by a clarinettist while performing music. This includes both a measurement apparatus with sensors and a software toolbox for analysis. Several of the components used here can also be applied in other musical contexts. Applied to the clarinet, the instrument sensors record blowing pressure, reed position, tongue contact and sound pressures in the mouth, mouthpiece and barrel. Radiated sound and multiple videos are also recorded to allow details of the embouchure and the instrument’s motion to be determined. The software toolbox can synchronise measurements from different devices, extract time-variable descriptors, segment by notes and excerpts, and summarise descriptors per note, phrase or excerpt. An example of its application is given showing how to compare performances from different musicians.Measuring fine-grained physical interaction between the human player and the musical instrument can significantly improve our understanding of music performance. This article presents a Musical Instrument Performance Capture and Analysis Toolbox (MIPCAT) that can be used to capture and to process the physical control variables used by a clarinettist while performing music. This includes both a measurement apparatus with sensors and a software toolbox for analysis. Several of the components used here can also be applied in other musical contexts. Applied to the clarinet, the instrument sensors record blowing pressure, reed position, tongue contact and sound pressures in the mouth, mouthpiece and barrel. Radiated sound and multiple videos are also recorded to allow details of the embouchure and the instrument’s motion to be determined. The software toolbox can synchronise measurements from different devices, extract time-variable descriptors, segment by notes and excerpts, and summarise descriptors per note, phrase or excerpt. An example of its application is given showing how to compare performances from different musicians.</p>
"I'm something of an untrained, unofficial cultural anthropologist myself. Ihave a business interviewing people to capture their personal histories. I'm always interested how people fit into their world and how they affect their world. I'm a graphic designer who works in the same building as the printing presses that I recorded. Iwalk past the presses every day on my way to talk to the folks in the prepress department. I'm on friendly but not drinking terms with the pressmen. I'm a friend with the prepress manager. Three Heidelberg presses are installed side by side in an open warehouse-like room. The presses are about twenty feet long and about five feet high. With their series of four humps or mounds where each printing cylinder is located, the presses remind one of giant, gray, mechanical caterpillars. Each press has a cyan cylinder, a magenta cylinder, a yellow cylinder and a black cylinder – so the humps are brightly colored. The presses are well lit by banks of fluorescent lights hanging from the ceiling over each press. When you walk into the press room you hear the sound of rock music blaring from a boom box radio mixed with the general din of the presses. It is only when you walk up to a press like Idid for the recordings that you really start to hear the individual strains of clicking, clacking and mechanical, syncopated chattering. When I made my recordings I was intrigued by the subtle variations in the sounds produced by these machines that aren't apparent when you first walk through the door. The pressmen were kind enough to allow me to walk right up to the presses and poke my microphone quite close to the rotating press cylinders. Iuse a Danish Pro Audio microphone about the size of a pencil eraser. An extremely sensitive mic with the capacity for capturing loud sounds such as the presses up close. Rotating the mic to one side or the other focused on the unique sounds coming from one cylinder or the other." [Kevin/KMerrell]18 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice
"I'm something of an untrained, unofficial cultural anthropologist myself. Ihave a business interviewing people to capture their personal histories. I'm always interested how people fit into their world and how they affect their world. I'm a graphic designer who works in the same building as the printing presses that I recorded. Iwalk past the presses every day on my way to talk to the folks in the prepress department. I'm on friendly but not drinking terms with the pressmen. I'm a friend with the prepress manager. Three Heidelberg presses are installed side by side in an open warehouse-like room. The presses are about twenty feet long and about five feet high. With their series of four humps or mounds where each printing cylinder is located, the presses remind one of giant, gray, mechanical caterpillars. Each press has a cyan cylinder, a magenta cylinder, a yellow cylinder and a black cylinder – so the humps are brightly colored. The presses are well lit by banks of fluorescent lights hanging from the ceiling over each press. When you walk into the press room you hear the sound of rock music blaring from a boom box radio mixed with the general din of the presses. It is only when you walk up to a press like Idid for the recordings that you really start to hear the individual strains of clicking, clacking and mechanical, syncopated chattering. When I made my recordings I was intrigued by the subtle variations in the sounds produced by these machines that aren't apparent when you first walk through the door. The pressmen were kind enough to allow me to walk right up to the presses and poke my microphone quite close to the rotating press cylinders. Iuse a Danish Pro Audio microphone about the size of a pencil eraser. An extremely sensitive mic with the capacity for capturing loud sounds such as the presses up close. Rotating the mic to one side or the other focused on the unique sounds coming from one cylinder or the other." [Kevin/KMerrell]18
GUITAR-FX-DIST: A Dataset of Processed Guitar Recordings for Music Research - (Mono Continuous)
<p><strong>GUITAR-FX-DIST</strong> is a dataset of electric guitar recordings processed with overdrive, distortion and fuzz audio effects. It was developed for research in guitar effects detection, classification and parameters estimation. The dataset is also useful for research on automatic music transcription, intelligent music production, signal processing or effects modelling. It contains both unprocessed and processed recordings.</p> <p>The dataset is split into 4 sub-datasets: Mono Continuous, Mono Discrete, Poly Continuous, Poly Discrete</p> <p> </p> <p><strong>Authors:</strong></p> <p>Marco Comunità - <a href="http://c4dm.eecs.qmul.ac.uk/">Centre for Digital Music</a>, Queen Mary University of London</p> <p> </p> <p><strong>Reference:</strong></p> <p>If you make use of GUITAR-FX-DIST, please cite the following publication:</p> <pre><code>@article{comunità2021guitar, title={Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks}, author={Comunità, Marco and Stowell, Dan and Reiss, Joshua D.}, journal={Journal of the Audio Engineering Society}, year={2021}, volume={69}, number={7/8}, pages={594-604}, doi={}, month={July} }</code></pre> <p> </p> <p><strong>Dataset Snapshot:</strong></p> <ul> <li><strong>Size:</strong> ~550k samples (~305 hours) + 550k mel spectrograms</li> <li><strong>Audio Format:</strong> WAV - 44.1kHz, 16bit, mono, -6dBFS</li> <li><strong>Mel-Spectrogram Format:</strong> NPY - 128 frequency bands, sample rate 22050Hz, window length 1024, hop size 512,</li> <li><strong>Effects:</strong> 14 between overdrive, distortion and fuzz</li> <li><strong>Unprocessed recordings</strong> <ul> <li>624 monophonic notes</li> <li>420 polyphonic (2, 3 and 4 notes intervals and chords)</li> <li>2 guitars, with up to 2 pick-up settings and up to 3 plucking styles (finger pluck - hard, finger pluck - soft, pick) <ul> <li>Schecter Diamond C-1 Classic</li> <li>Chester Stratocaster</li> </ul> </li> </ul> </li> <li><strong>Samples length:</strong> 2 sec</li> </ul> <p> </p> <p><strong>Unprocessed Recordings:</strong></p> <p>The original (unprocessed) recordings are from the <a href="https://www.idmt.fraunhofer.de/en/business_units/m2d/smt/audio_effects.html">IDMT-SMT-Audio-Effects</a> dataset.</p> <p>For details please refer to the website and the accompagning publication:</p> <p><em>Stein, Michael; Abeßer, Jakob; Dittmar, Christian; Schuller, Gerald: Automatic Detection of Audio Effects in Guitar and Bass Recordings. Proceedings of the AES 128th Convention, 2010.</em></p> <p> </p> <p><strong>Processed Recordings:</strong></p> <p>The processed recordings are divided into 4 sub-datasets which are named depending on the unprocessed recordings used (monophonic or polyphonic) and on the settings' values (discrete or continuous).</p> <p>The sub-datasets are called: Mono Discrete, Poly Discrete, Mono Continuous, Poly Continuous</p> <p>Mono Discrete and Poly Discrete use a discrete set of combinations selected as the most common and representative settings a person might use (see README file for details).</p> <p>For Mono Continuous and Poly Continuous both unprocessed samples as well as settings’ values are drawn from a uniform distribution (10000 samples for each effect).</p> <p>Samples:</p> <ul> <li>Mono Discrete: ~160k</li> <li>Poly Discrete: ~110k</li> <li>Mono Continuous: 140k</li> <li>Poly Continuous: 140k</li> </ul> <p> </p> <p><strong>Scripts:</strong></p> <p>The dataset includes the MATLAB scripts used to generate the samples</p>
Multimodal datataset of EEG, ECG and audio recordings of individual musicians playing emotional music
<p>This dataset contains EEG, ECG and audio recordings of 11 individual musicians playing emotional music on their instrument. The dataset consists of two parts:</p> <ol> <li> <p>Experiment: Musicians’ self-reported ratings, audio recordings, and physiological recordings where the 11 expert musicians were asked to play at least 4~2-minute unfamiliar (non-popularly known) musical pieces. Participants were asked to play at least once one of the following emotions: happiness, sadness, relaxation, and anger. For the physiological recordings EEG, ECG, and GSR signals were recorded. Each musican’s data is denoted by MS_ followed by the order in which they were recorded.</p> </li> <li> <p>Self-report questionnaire: A self-assessment questionnaire and their answers where 11 expert musicians were asked to rate musical pieces recorded based on:</p> <ol> <li> <p>Objective valence & arousal they felt the piece had;</p> </li> <li> <p>Felt valence & arousal during playing.</p> </li> </ol> </li> </ol> <p> </p> <p>For a more detailed explanation of the dataset, its recording procedure, and its contents, see </p> <p>L. Turchet, B. O'Sullivan, R. Ortner & C. Gugher (2024). Emotion Recognition of Playing Musicians from EEG, ECG, and Acoustic Signals. IEEE Transactions on Human-Machine Systems.</p> <p><strong> </strong></p> <h2>File Listing</h2> <p>The following files are available (each explained in more detail below):</p> <div> <table> <tbody> <tr> <td> <p>Name</p> </td> <td> <p>Format</p> </td> <td> <p>Contents</p> </td> </tr> <tr> <td> <p>EEG_ECG_data_for_each_musician</p> </td> <td> <p>mat</p> </td> <td> <p>This folder contains the raw EEG, ECG, & GSR data for all 11 musicians for each piece they played as well as a resting state recording, which was recorded while a neutral audio stimulus was played.</p> </td> </tr> <tr> <td> <p>audio_data_for_each_musician</p> </td> <td> <p>wav, JSON, csv</p> </td> <td> <p>This folder contains 3 subfolders: <br>1) wav_audio_files_original: This is the raw audio data recorded for each musician and includes all 56 pieces included in the paper reported above, please see below regarding rejected trials.</p> <p>2) wav_audio_files (original split_into_3_parts): Here, the 56 pieces are appropriately split into 3 separate parts.<br><br>3) analysis_audio_files: This folder contains the acoustic features extracted, 1714 acoustic features were extracted from each split trial. Each result is stored in .JSON, however, the collated results can be seen in all_results.csv.</p> </td> </tr> <tr> <td> <p>self_report_questionnaire</p> </td> <td> <p>pdf, xls</p> </td> <td> <p>Two files exist in this folder:<br>1. The self-reported questionnaire given to the musicians of the questionnaire during the experiment.<br>2. </p> </td> </tr> </tbody> </table> </div> <p><strong><br><br></strong></p> <h2>File Details</h2> <p><strong> </strong></p> <h3>EEG_ECG_data_for_each_musician</h3> <p>These are the original raw data recordings. EEG data were recorded using a g.GAMMAcap2 by g.tec Medical Engineering, a 64-channel cap with g.SCARABEO active electrodes, with two g.GAMMAsys reference active ear clip electrodes. Two g.GAMMAbox electrode connector boxes were used to connect the active electrodes to two g.USBamp biosignal amplifiers with a sampling frequency of 256 Hz.<br><br>The following 31 EEG channels were used: Fp1, Fp2, AFz, AF3, AF4, AF7, AF8, Fz, F3, F4, F7, F8, Cz, C3, C4, CP3, CP4, CP5, CP6, P1, P2, P3, P4, P5, P6, P7, P8, PO7, PO8, O1, and O2. AFz was used as a ground electrode and Cz was used as a re-reference electrode. The right-side ear clip electrode was used as a reference electrode. </p> <p><br>ECG data were recorded using a single g.GAMMAclip active electrode clip connected directly to the g.GAMMAbox, sharing the same ground electrode with the EEG cap and placed on position V4 of the subjects.<br><br>GSR data was recorded using the g.GSRsensor² box which contains two small dry electrodes placed underneath the participant’s toes (due to the amount of hand movement required for playing an instrument). The g.GSRsensor² was connected directly to a g.GAMMAbox, using jumper cables connected to different ports in the g.USBAMPs to share the same reference and ground electrodes as the EEG cap. </p> <p><strong> </strong></p> <p>The locations of the channels and their corresponding number in the raw data is as follows:</p> <div> <table> <tbody> <tr> <td> <p>Channel Number</p> </td> <td> <p>Channel Name</p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>Time series</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>AF3</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>AF4</p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>AF7</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>AF8</p> </td> </tr> <tr> <td> <p>6</p> </td> <td> <p>CP3</p> </td> </tr> <tr> <td> <p>7</p> </td> <td> <p>CP4</p> </td> </tr> <tr> <td> <p>8</p> </td> <td> <p>CP5</p> </td> </tr> <tr> <td> <p>9</p> </td> <td> <p>CP6</p> </td> </tr> <tr> <td> <p>10</p> </td> <td> <p>P1</p> </td> </tr> <tr> <td> <p>11</p> </td> <td> <p>P2</p> </td> </tr> <tr> <td> <p>12</p> </td> <td> <p>P5</p> </td> </tr> <tr> <td> <p>13</p> </td> <td> <p>P6</p> </td> </tr> <tr> <td> <p>14</p> </td> <td> <p>P7</p> </td> </tr> <tr> <td> <p>15</p> </td> <td> <p>P8</p> </td> </tr> <tr> <td> <p>16</p> </td> <td> <p>O1</p> </td> </tr> <tr> <td> <p>17</p> </td> <td> <p>O2</p> </td> </tr> <tr> <td> <p>18</p> </td> <td> <p>Cz</p> </td> </tr> <tr> <td> <p>19</p> </td> <td> <p>Fp1</p> </td> </tr> <tr> <td> <p>20</p> </td> <td> <p>Fp2</p> </td> </tr> <tr> <td> <p>21</p> </td> <td> <p>F3</p> </td> </tr> <tr> <td> <p>22</p> </td> <td> <p>F4</p> </td> </tr> <tr> <td> <p>23</p> </td> <td> <p>F7</p> </td> </tr> <tr> <td> <p>24</p> </td> <td> <p>F8</p> </td> </tr> <tr> <td> <p>25</p> </td> <td> <p>C3</p> </td> </tr> <tr> <td> <p>26</p> </td> <td> <p>C4</p> </td> </tr> <tr> <td> <p>27</p> </td> <td> <p>P3</p> </td> </tr> <tr> <td> <p>28</p> </td> <td> <p>P4</p> </td> </tr> <tr> <td> <p>29</p> </td> <td> <p>PO7</p> </td> </tr> <tr> <td> <p>30</p> </td> <td> <p>PO8</p> </td> </tr> <tr> <td> <p>31</p> </td> <td> <p>AFz</p> </td> </tr> <tr> <td> <p>32</p> </td> <td> <p>GSR</p> </td> </tr> <tr> <td> <p>33</p> </td> <td> <p>ECG</p> </td> </tr> </tbody> </table> </div> <p><strong><br><br></strong></p> <h3>audio_data_for_each_musician/wav_audio_files_original</h3> <p>This folder contains all of the raw audio files recorded during the experiment. All pieces were recorded using the software Audacity and exported as WAV files encoded with a bit depth of 32-bits and a sampling rate of 44.1 kHz. 56 of the included trials are present in this folder.</p> <p><strong> </strong></p> <h3>audio_data_for_each_musician/wav_audio_files (original split_into_3_parts)</h3> <p>This folder contains the above mentioned 56 raw audio pieces separated into 3 appropriately sized recordings.</p> <p><strong> </strong></p> <p>audio_data_for_each_musician/analysis_audio_files<br><br>This folder contains the 1714 acoustic features from each of the 3 separated trials from 56 accepted pieces, these are denoted by MS_ followed by the order in which the musicians were recorded and the order in which the trails were split, the individual features are in JSON format. Within this folder, we have collated all results in a csv file (all_results.csv) where the columns show the intended emotion, trial name and number, followed by the names of the acoustic features. </p> <p><strong> </strong></p> <p>self_report_questionnaire</p> <p>This pdf is the questionnaire which each musician was given following each trial relating to the emotions communicated and felt during each trial. Most* questions in the questionnaire were multiple-choice and speak pretty much for themselves. The answers for which were collated and are described below.</p> <p><strong> </strong></p> <p>self_report_questionnaire_anwsers</p> <p>This .xls file contains the results of all the musicians self-reported ratings from the above described questionnaire. </p> <div> <table> <tbody> <tr> <td> <p>Column name</p> </td> <td> <p>Description</p> </td> </tr> <tr> <td> <p>Subject</p> </td> <td> <p>The subject code of the musician, denoted by MS_</p> </td> </tr> <tr> <td> <p>recording_filename</p> </td> <td> <p>The name of the trial denoted by MS_01_ followed by the trial number.</p> </td> </tr> <tr> <td> <p>intended_emotion</p> </td> <td> <p>The intended emotion the musician was instructed to communicate. The emotions are as follows:</p> <ol> <li> <p>Angry</p> </li> <li> <p>Sad</p> </li> <li> <p>Relaxed</p> </li> <li> <p>Happy</p> </li> </ol> <p>They are intended to be reported by using the valence-arousal space.</p> </td> </tr> <tr> <td> <p>valence_communicated</p> </td> <td> <p>The valence rating (integer between 1 and 5), that participants were asked to objectively rate how they thought the music played would be perceived. </p> </td> </tr> <tr> <td> <p>arousal_communicated</p> </td> <td> <p>As above, but relating to arousal rather than valence.</p> </td> </tr> <tr> <td> <p>valence_felt</p> </td> <td> <p>The valence rating (integer between 1 and 5), that participants were asked to rate how they felt while playing the piece.</p> </td> </tr> </tbody> </table> </div> <p> </p> <div> <table> <tbody> <tr> <td> <p>arousal_felt</p> </td> <td> <p>As above, but relating to arousal rather than valence.</p> </td> </tr> <tr> <td> <p>instrument_played</p> </td> <td> <p>The instrument played for each trial.</p> </td> </tr> </tbody> </table> </div> <p><strong><br><br></strong></p>
Immersive Audio Remixing of Mono Recordings of the 1950s and 1960s: Rediscovering Musical Treasures from the Past
<p><strong>IMPORTANT NOTE</strong></p> <p>The song <em>B-A-B-Y</em> was written by Isaac Hayes and David Porter, then also arranged by Booker T. Jones and Steve Cropper, and performed by Carla Thomas in 1966.</p> <p>The following binaural sound remixes of <em>B-A-B-Y</em> have been performed in a research context, with four various HRTF profiles coming from the Listen HRTF Database by Ircam, for the AES research paper “Immersive Audio Remixing of Mono Recordings of the 1950s and 1960s: Rediscovering Musical Treasures from the Past”, presented at the 156th AES Convention on June 15-17, 2024, in Madrid, Spain. </p> <p>I, the undersigned Jean Viardot, the first author of this paper, testify that nothing about the composition, the arrangement, or any element of the musical content of the song, has been manipulated to obtain this remix. Only the sound rendering of a digitalized version of the original master has been manipulated and modified to address the research questions being stated in the abovementioned paper.</p> <p>To date (5/11/2024), I have not made any profit with this remix, and I forbid anyone to publish it without my permission. For any intention to publish, broadcast, or market this remix, please contact me beforehand.</p>
Music Therapy and Pre-recorded Music on Patients and Caregivers in the Emergency Unit
ClinicalTrials.gov study NCT06510153. IPD Sharing: NO. Countries: 1. Publications: 1.
CT-Audiolink : Narrative and music recordings - http://ct-audiolink.gr
<p>Recordings of Greek, Turkish and Pomak language narratives and music (songs, fairy tales, traditional recipes etc.)<br> These recordings took place in situ in various locations at the region of Thrace from local speakers and musicians.</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>
Effects of Recorded Music in Heart Failure Patients
ClinicalTrials.gov study NCT02394938. IPD Sharing: Not stated. Countries: 1. Publications: 7.
Effects of Recorded Music on Clinical and EEG Seizure Activity
ClinicalTrials.gov study NCT05289934. IPD Sharing: NO. Countries: 1. Publications: 4.
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.