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90 results for “Music Listening”
Perceptions of Diversity in Electronic Music: the Impact of Listener, Artist, and Track Characteristics
<p>Data Release and facsimile of the survey, presented in the submission 3238 to the CSCW 2021 conference.</p> <p> </p>
A dataset recording joint EEG-fMRI during affective music listening
Open the record for dataset details and reuse information.
Music Streams Labelled with Listening Situation - [User/Track/Device/Situation] Dataset
<p>This is a contextual music dataset labeled with the listening situation associated with each stream. Each stream is composed of the user, track, and device data labelled with a situation. The dataset is collected from Deezer for the period of August 2019 from France and Brazil. The dataset is composed of 3 subsets of situations corresponding to 4, 8, and 12 different situations. The situations are extracted based on keyword matching with the associated playlist title in the Deezer catalog. The full set of situational tags are: "<strong>work, gym, party, sleep | morning, run, night, dance | car, train, relax, club"</strong>.</p> <p>Each instance contains the track/user/deviice triplets, and a situational tag indicating that this user listens to the track in the associated situation wth the corresponding data recieved from the device. The device data contain: "l<strong>inear-time, linear-day, circular-time X, circular-time Y,circular-day X, circular-day Y, device-type, network-type</strong>". The users are represented as <strong>embeddings</strong> based on their listening history computed through the matrix factorization of the user/track matrix. Additionally, the users are also represented with their demographic data of : "<strong>age, country, gender</strong>".</p> <p>The creation of the dataset and our experimental results are described in the paper: Karim M. Ibrahim, Elena V. Epure, Geoffroy Peeters, and Gaël Richard. "Audio Autotagging as Proxy for Contextual MusicRecommendation" [Under Revision]. The source code of the paper is available here: <a href="https://github.com/KarimMibrahim/Situational_Session_Generator.git">https://github.com/KarimMibrahim/Situational_Session_Generator.git</a></p> <p>The dataset is composed of the media_id which is the ID of the track in the Deezer catalog. The 30 seconds track previews used to train the model in the paper can be accessed through the Deezer API: <a href="https://developers.deezer.com/api">https://developers.deezer.com/api</a>. Each user is represented with an <strong>anonymized</strong> <strong>user_id</strong> which is associated with the user embedding available in the user_embeddings.npy file. Note: The index of the embeddings in the user_embeddings arrary corresponds to the user_id, i.e. user_id = 100 have its embeddings at user_embeddings[100]. </p> <p>Finally, the dataset also contains the splits used in our experiments. Our splits were conditioned by one of three conditions: <em>ColdTrack</em> (no overlap of tracks between the splits), <em>ColdUser</em> (no overlap of users between the splits), and <em>WarmCase</em> (overlaps allowed). Each condition is split into 4 subsets for cross-validation marked with a "<strong>fold</strong>" number in each condition. </p>
Listening preferences for the different reproduction systems Stereo, Surround, and Wave Field Synthesis in the context of popular music
<p>We did a paired comparison preference test where listeners rated their listening preference for four different pop musical pieces presented by WFS, stereo or surround. The musical pieces were all mixed by the same person in order to try to minimize the influence of the mix on the ratings, but still trying to get the best out of every system, see [1] for details. The mixes are available at https://doi.org/10.14279/depositonce-5173.</p> <p>Here, we provide the results of the 22 listeners that participated in the experiment together with an analysis which calculates a Bradley-Terry-Luce model after Wickelmayer et al. [2].</p> <p>[1] Hold, C., Wierstorf, H., Raake, A. (2016), “The Difference Between Stereophony and Wave Field Synthesis in the Context of Popular Music,” 140th AES Convention, Paper 9533</p> <p>[2] https://cran.r-project.org/web/packages/eba/index.html</p>
Help Me study! Music Listening Habits While Studying (Dataset)
<p>This repository contains the raw data used for a research study that examined university students' music listening habits while studying. There are two experiments in this research study. Experiment 1 is a retrospective survey, and Experiment 2 is a mobile experience sampling research study.</p><p>This repository contains five Microsoft Excel files with data obtained from both experiments. The files are as follows:</p><ul><li><i>onlineSurvey_raw_data.xlsx</i></li><li><i>esm_raw_data.xlsx</i></li><li><i>esm_music_features_analysis.xlsx</i></li><li><i>esm_demographics.xlsx</i></li><li><i>index.xlsx</i></li></ul><p><strong>Files Description</strong></p><p><i><strong>File: onlineSurvey_raw_data.xlsx</strong></i></p><p>This file contains the raw data from Experiment 1, including the (anonymised) demographic information of the sample. The sample characteristics recorded are:</p><ul><li>studentship</li><li>area of study</li><li>country of study</li><li>type of accommodation a participant was living in</li><li>age</li><li>self-identified gender</li><li>language ability (mono- or bi-/multilingual)</li><li>(various) personality traits</li><li>(various) musicianship</li><li>(various) everyday music uses</li><li>(various) music capacity</li></ul><p>The file also contains raw data of responses to the questions about participants' music listening habits while studying in real life. These pieces of data are:</p><ul><li>likelihood of listening to specific (rated across 23) music genres while studying and during everyday listening.</li><li>likelihood of listening to music with specific acoustic features (e.g., with/without lyrics, loud/soft, fast/slow) music genres while studying and during everyday listening.</li><li>general likelihood of listening to music while studying in real life.</li><li>(verbatim) responses to participants' written responses to the open-ended questions about their real-life music listening habits while studying.</li></ul><p><i><strong>File: esm_raw_data.xlsx</strong></i></p><p>This file contains the raw data from Experiment 2, including the following variables:</p><ul><li>information of the music tracks (track name, artist name, and if available, Spotify ID of those tracks) each participant was listening to during each music episode (both while studying and during everyday-listening)</li><li>level of arousal at the onset of music playing and the end of the 30-minute study period</li><li>level of valence at the onset of music playing and the end of the 30-minute study period</li><li>specific mood at the onset of music playing and the end of the 30-minute study period</li><li>whether participants were studying</li><li>their location at that moment</li><li>(if studying) whether they were studying alone</li><li>(if studying) the types of study tasks</li><li>(if studying) the perceived level of difficulty of the study task</li><li>whether participants were planning to listen to music while studying</li><li>(various) reasons for music listening</li><li>(various) perceived positive and negative impacts of studying with music</li></ul><p>Each row represents the data for a single participant. Rows with a record of a participant ID but no associated data indicate that the participant did not respond to the questionnaire (i.e., missing data).</p><p><i><strong>File: esm_music_features_analysis.xlsx</strong></i></p><p>This file presents the music features of each recorded music track during both the study-episodes and the everyday-episodes (retrieved from Spotify's "Get Track's Audio Features" API). These features are:</p><ul><li>energy level</li><li>loudness</li><li>valence</li><li>tempo</li><li>mode</li></ul><p>The contextual details of the moments each track was being played are also presented here, which include:</p><ul><li>whether the participant was studying</li><li>their location (e.g., at home, cafe, university)</li><li>whether they were studying alone</li><li>the type of study tasks they were engaging with (e.g., reading, writing)</li><li>the perceived difficulty level of the task</li></ul><p><i><strong>File: esm_demographics.xlsx</strong></i></p><p>This file contains the demographics of the sample in Experiment 2 (N = 10), which are the same as in Experiment 1 (see above).</p><p>Each row represents the data for a single participant. Rows with a record of a participant ID but no associated demographic data indicate that the participant did not respond to the questionnaire (i.e., missing data). </p><p><i><strong>File: index.xlsx</strong></i></p><p>Finally, this file contains all the abbreviations used in each document as well as their explanations.</p>
Code to reproduce the figures in the paper 'Listener Preference for Different Reproduction Systems and Mixes in Popular Music'
<p>In this upload you find all the scripts and data you need in order to reproduce<br> the figure from the paper Wierstorf et al., "Listener Preference for Different<br> Reproduction Systems and Mixes in Popular Music" [1].</p> <p>Software Requirements<br> ---------------------</p> <p>For the statistic analysis you will need [python](https://www.python.org) and<br> [R](https://www.r-project.org). I have used python 3.5.2 and R 3.2.3 for<br> published analysis.</p> <p>Under R you need to install the [eba](https://cran.r-project.org/package=eba)<br> package, which implements the Bradley-Terry-Luce model. You can install it in R<br> by running `install.packages("eba")`.</p> <p>Under python you have to install pandas and numpy.</p> <p>Reproduce figures<br> -----------------</p> <p>All figures were plotted using gnuplot 5.0. Every figure folder has an<br> ``figXX.plt`` (replace ``XX`` by the figure number) file that you can execute<br> and you will get the resulting pdf file. For Fig. 5 up to Fig. 9, also a<br> ``figXX.sh`` file is provided, that will rerun the statistical analysis of the<br> data presented in the figures.</p> <p>References<br> ----------</p> <p>[1] H. Wierstorf, C. Hold, A. Raake, "Listener Preference for Different<br> Reproduction Systems and Mixes in Popular Music," J. Audio. Eng. Soc, submitted. <br> </p>
Effects of personalized music listening on post-stroke cognitive impairment: A randomized controlled trial
<p><strong><span>Background and purpose:</span></strong><span> Previous studies have suggested that music listening has the potential to positively affect mood and cognitive functions in individuals with <a name="_Hlk140153246"></a>post-stroke cognitive impairment (PSCI), with a preference for self-selected music likely to yield better outcomes. However, there is insufficient clinical evidence to suggest the use of music listening in routine rehabilitation care to treat PSCI. This randomized control trial (RCT) aims to investigate the effects of personalized music listening on mood improvement, <a name="_Hlk140153263"></a>activities of daily living (ADLs), and cognitive functions in individuals with PSCI.</span></p> <p><strong><span>Materials and methods:</span></strong><span> A total of 34 patients with PSCI were randomly assigned to either the music group or the control group. Patients in the music group underwent a three-month personalized music-listening intervention. The intervention involved listening to a personalized playlist tailored to each individual's cultural, ethnic, and social background, life experiences, and personal music preferences. In contrast, the control group patients listened to white noise as a placebo. Cognitive function, neurological function, mood, and ADLs were assessed. </span></p> <p><strong><span>Results:</span></strong><strong><span> </span></strong><span>After three months of treatment, the music group showed significantly higher <a name="_Hlk140153278"></a>Montreal Cognitive Assessment (MoCA) scores compared to the control group (<em>p=</em>0.027), particularly in the domains of delayed memory (<em>p=</em>0.019) and orientation (<em>p=</em>0.023). Moreover, the music group demonstrated significantly better scores in <a name="_Hlk140153297"></a>National Institute of Health Stroke Scale (NIHSS) (<em>p=</em>0.008), <a name="_Hlk140153304"></a>Barthel Index (BI) (<em>p=</em>0.019), and <a name="_Hlk140153315"></a>Zarit Caregiver Burden Interview (ZBI) (<em>p=</em>0.008) compared to the control group. No effects were found on mood as measured by the Hamilton Rating Scale for Anxiety (HAMA) and the Hamilton depression scale (HAMD).</span></p>
IKA CI Music Preprocessing Listening Experiment Stimuli (2022)
<p><strong>IKA CI Music Preprocessing Listening Experiment Stimuli (2022)</strong></p> <p>This dataset contains the audio stimuli that have been presented to both cochlear implant (CI) and normal hearing (NH) listeners in the listening experiments in a study named</p> <p><em>“A Subjective Evaluation of Different Music Preprocessing Approaches in CI Listeners”</em></p> <p>It comprises the excerpts from the <strong>IKA CI Pop Music Dataset (IKA-CI-PMD)</strong> (<a href="https://doi.org/10.5281/zenodo.7060282">10.5281/zenodo.7060282</a>) both as unprocessed references and as processed versions where different music preprocessing strategies have been<br> applied.</p> <p>The <strong>IKA CI Pop Music Dataset</strong> is a dataset of music excerpts that has especially compiled to evaluate different music signal preprocessing strategies for CI listeners. The excerpts taken are from the <strong>MedleyDB </strong>multitrack dataset (<a href="https://medleydb.weebly.com/">https://medleydb.weebly.com/</a>) curated by <a href="https://steinhardt.nyu.edu/marl/research/resources/medleydb">Rachel Bittner et. al.</a>.</p> <p>This dataset is split into a 3-piece “training” set (excerpts T01 to T03) that has been used to familiarize the listeners with the experimental setup, and a 12-piece “test” set (E01 to E12) used in actual experiments.</p> <p>The following music preprocessing strategies are included:</p> <ul> <li>HPCA+P: Harmonic/percussive sound separation (HPSS) combined with PCA-based spectral complexity reduction [1]</li> <li>Cspl and Dspl: DNN-based remix of the harmonic and percussive portions of 4 source stems [2]</li> <li>HALCA: Remix based on a probabilistic model for melody extraction using shift invariant kernels in CQT domain [3] (accompaniment attenuated by 12 dB)</li> <li>MT remix: Oracle remixes of multitrack stems (other accompaniment attenuated by 12 dB)</li> </ul> <p>All stimuli are normalized to a loudness level of -27 LUFS. The signals are stored in the lossless FLAC format. The files contain stereo signals, where both channels are identical.</p> <p>The dataset has been compiled at the Ruhr University Bochum <a href="https://www.ruhr-uni-bochum.de/ika/index_en.html">Institute of Communication Acoustics</a> in 2022 by Johannes Gauer (johannes.gauer@rub.de) in collaboration with the fellow researchers Anil Nagathil, Benjamin Lentz, and Rainer Martin. Like MedleyDB and the IKA CI Pop Music Dataset, it is licensed under a <a href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</p> <p>For further details on the included excerpts refer to the IKA CI Pop Music Dataset (IKA-CI-PMD) (<a href="https://doi.org/10.5281/zenodo.7060282">10.5281/zenodo.7060282</a>).</p> <p>[1] B. Lentz, A. Nagathil, J. Gauer, and R. Martin, “Harmonic/Percussive sound separation and spectral complexity reduction of music signals for cochlear implant listeners,” in Proc IEEE Int Conf Acoust Speech Signal Process ICASSP, Barcelona, Spain, May 2020, pp. 8713–8717.</p> <p>[2] J. Gauer, A. Nagathil, K. Eckel, D. Belomestny, and R. Martin, “A versatile deep-neural-network-based music preprocessing and remixing scheme for cochlear implant listeners,” J. Acoust. Soc. Am., vol. 151, no. 5, pp. 2975–2986, May 2022.</p> <p>[3] B. Fuentes, R. Badeau, and G. Richard, “Harmonic Adaptive Latent Component Analysis of Audio and Application to Music Transcription,” IEEE Trans. Audio Speech Lang. Process., vol. 21, no. 9, pp. 1854–1866, Sep. 2013.</p>
Listening to Calming Music
ClinicalTrials.gov study NCT06710886. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Neural dynamics of predictive timing and motor engagement in music listening
Open the record for dataset details and reuse information.
Cochlear Implant Compression Optimization for Music Listening - Maplaw and AGC
Open the record for dataset details and reuse information.
Effect of Music Listening on Early Mobility in Children Undergoing Appendectomy
ClinicalTrials.gov study NCT07128758. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Music Listening on Preoperative Anxiety in Female Pelvic Medicine and Reconstructive Surgery
ClinicalTrials.gov study NCT03651310. IPD Sharing: UNDECIDED. Countries: 1. Publications: 5.
Listening Music, Drawing on Coping With Dysmenorrhea Complaints of Nursing Students
ClinicalTrials.gov study NCT06027489. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Reducing Pain With Focused Music Listening
ClinicalTrials.gov study NCT05267795. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Does Listening to Music Alter the Running Mechanics?
ClinicalTrials.gov study NCT03506282. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Music Listening as a Postanesthesia Care Unit (PACU) Nursing Intervention
ClinicalTrials.gov study NCT04596917. IPD Sharing: NO. Countries: 1. Publications: 14.
Music Listening for Mental Health Recovery After Stroke
ClinicalTrials.gov study NCT07127159. IPD Sharing: YES. Countries: 1. Publications: 6.
Letting Children Listen to Music During Computed Tomography
ClinicalTrials.gov study NCT06086509. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Effect of Intraoperative Music Listening on Sevoflurane Consumption and Recovery Parameters
ClinicalTrials.gov study NCT02220452. IPD Sharing: Not stated. Countries: 1. Publications: 7.
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OpenNeuro
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