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28 results for “improvisation”
EEG: Improvisation and Musical Structures
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Measuring individual and group flow in collaborative improvisational dance.
<p>Flow is a state of being fully absorbed and experiencing feelings of energised focus, deep involvement, and success in the process of doing things. Flow plays a vital role in innovation and creativity, as all such processes require high intrinsic motivation to break through to a new level of complexity of thoughts and ideas, while the social environment rarely provides sufficient extrinsic rewards to motivate people to extensive creative work. Meanwhile, the vast majority of creative activities have a primarily social character: e.g. theatre making, music, and dancing. Thus, group flow became central in group creativity research.</p> <p>Group flow shares many aspects with individual flow, but inevitably has differences, due to its collaborative nature. In this study, we compare individual and group flow in dance improvisation, to explore the cognitive processes and strategies underlying group improvisation and their relation to flow experience; in particular, those that might support the aspects of group flow that are dependent upon understanding the other group members’ states and intentions.</p> <p>To assess flow experience, we used a video-stimulated recall method, <em>Flow </em>(Łucznik, Loesche, 2017), which allowed participants to mark on the video-recording of the activity those moments when they remembered experiencing flow. We identified group flow as the moments when then the majority of a group declared themselves as being in flow.</p> <p>This dataset consists of the data and analysis used in the 'Measuring individual and group flow in collaborative improvisational dance.' article (in press).</p>
Creative Flow in Dance: Consensual Assessment of Creative Outcomes of Dance Group Improvisation
<p>Group flow refers to peak experience when a group is performing at its highest level of abilities. This study examined the relationship between the group flow experience of an improvising group of contemporary dancers and their creative outcomes evaluated by the consensual assessment method (CAT). The dataset consists of the data and analysis used in the "Creative Flow in Dance: Consensual Assessment of Creative Outcomes of Dance Group Improvisation (in press)."</p> <p>First, eight groups of four dancers were recruited to perform improvised dance scores for video recordings. The low and high-flow improvisations videoclips were selected based on dancers’ reports in the video-stimulated recall method, Flow (Łucznik, May, 2021). Subsequently, these videoclips were rated by 77 experts and 126 nonexperts using CAT on five dimensions: aesthetic appeal, technique demands, meaningfulness, coherence /collaboration of the group, and creativity.</p> <p>Experts rated high-flow improvisations as more creative than the low-flow ones. In comparison, nonexperts’ ratings of high and low-flow improvisation did not differ, although the reliability measures of nonexperts creativity judgments were noticeably lower than in the expert group. Regardless of expertise, creativity judgments were significantly related to all other CAT factors: aesthetic appeal, technique, meaningfulness and coherence of improvisation. The results provide evidence that group flow leads to higher creativity. Moreover, they suggest that subjective judgments of creativity of performance arts cannot be easily separated from judgments of their technical level, aesthetic appeal, meaningfulness or coherence of the group.</p>
DeepDance: Motion capture data of improvised dance (2019)
<p><em>When using this resource, please cite Wallace, B., Nymoen, K., Martin, C.P & Tøressen, J. DeepDance: Motion capture data of improvised dance (2019) (version 2.0). Zenodo 10.5281/zenodo.5838178</em></p> <p><strong>Abstract</strong></p> <p>This dataset comprises full-body motion capture of improvised dance as well as corresponding audio files. 30 dancers were recorded individually, improvising to six different audio files. The motion was captured in units of mm at 240Hz using a Qualisys infra-red optical system. The experiment was carried out at the University of Oslo in October 2019. For each dancer, 3 performances are recorded for each musical piece, resulting in 540 1-minute motion capture files. The dataset was collected for use as training data in deep learning for motion generation. This dataset also includes MATLAB code to visualize the motion capture files.</p> <p> </p> <p><strong>Music</strong></p> <ul> <li>Skarphedinsson, M. Wallace, B. (2019). “Song a”</li> <li>Skarphedinsson, M. Wallace, B. (2019). “Song b”</li> <li>Skarphedinsson, M. Wallace, B. (2019). “Song c”</li> <li>Skarphedinsson, M. Wallace, B. (2019). “Song d”</li> <li>Skarphedinsson, M. Wallace, B. (2019). “Song f”</li> <li>LaClair, J. Bounce. Jesse LaClair, (2018) <em>Referenced here as “Song e”</em></li> </ul> <p> </p> <p><strong>Data Description</strong></p> <p>The following data types are provided:</p> <ul> <li>Motion (marker position): Recorded with Qualisys Track Manager and saved as tab-separated .tsv files.</li> <li>Stimuli: audio .wav files containing 1 minute of the tracks described above.</li> <li>MATLAB script for animating the tsv files. (requires the MoCap Toolbox)</li> </ul> <p>Note: Recordings which contained errors such as missing markers have been replaced by subject 001. </p> <p><strong>Acknowledgements</strong></p> <p>This work was partially supported by the Research Council of Norway through its Centres of Excellence scheme, project number 262762.</p> <p> </p> <p><strong>Conflicts of Interest</strong></p> <p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p> <p> </p>
Human-Machine Music Improvisation Artefacts
<p>Corpus of artefacts from a study: Design Considerations for Human-AI Communication in Music Improvisation.</p> <p>A human musician and an AI music system performed together as a duo in an improvised music setting.</p> <p>Included here are: Audio recordings of the music performances, participants comments referencing specific points in the performance recording and interview transcriptions from a follow up interview. </p>
Improvising with Iliad
<p>Experimental live coding session, exploring correspondences between metrical patterns in live coding and Ancient Greek lyric.</p>
Sculpture "Mickiewicz after an Improvisation"
Sculpture "Mickiewicz after an Improvisation" by Wacław Szymanowski ID no.: MNK-II-rz-1235 Museum: The National Museum in Kraków https://muzea.malopolska.pl/en/objects-list/8 Object copyright: The National Museum in Kraków Digitalisation: RDW MIC, Małopolska's Virtual Museums project Source: Objaverse 1.0 / Sketchfab
Deep Gradient Reinforcement learning for Music Improvisation in cloud computing framework
<p><span>The improvised music is further rendered in the MIDI format. The Bach Chorales dataset with six different attributes relevant to musical compositions is employed in implementing the present research. The model was set up in a containerised cloud environment and controlled for smooth load distribution. Five different parameters, such as pitch frequency (PF), standard pitch delay (SPD), average distance between peaks (ADP), note duration gradient (NDG) and pitch class gradient (PCG) are leveraged to assess the quality of the improvised music.</span></p>
Test of the Safety, Effectiveness, & Acceptability of An Improvised Dressing for Sickle Cell Leg Ulcers in the Tropics
ClinicalTrials.gov study NCT04479618. IPD Sharing: YES. Countries: 1. Publications: 2.
Improvisational Movement for People With Memory Loss and Their Caregivers
ClinicalTrials.gov study NCT03333837. IPD Sharing: NO. Countries: 1. Publications: 90.
Data for: Design and testing of a sew-free origami mask for improvised respiratory protection
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Video Figure: Intelligent Agents and Networked Buttons Improve Free-Improvised Ensemble Music-Making on Touch-Screens
<p>This video figure is an overview of our study comparing two designs for network communications between touch-screen musical instruments played in free-improvised ensemble performances.</p> <p>The video shows an overview of the touch-screen app (PhaseRings) used in the study and each of the interface conditions.</p> <p>The abstract of the paper relating to this figure is as follows:</p> <p>We present the results of two controlled studies of free-improvised ensemble music-making on touch-screens. In our system, updates to an interface of harmonically-selected pitches are broadcast to every touch-screen in response to either a performer pressing a GUI button, or to interventions from an intelligent agent. In our first study, analysis of survey results and performance data indicated significant effects of the button on performer preference, but of the agent on performance length. In the second follow-up study, a mixed-initiative interface, where the presence of the button was interlaced with agent interventions, was developed to leverage both approaches. Comparison of this mixed-initiative interface with the always-on button-plus-agent condition of the first study demonstrated significant preferences for the former. The different approaches were found to shape the creative interactions that take place. Overall, this research offers evidence that an intelligent agent and a networked GUI both improve aspects of improvised ensemble music-making.</p>
Autism - Children's Improvisational Music Therapy Evaluation
ClinicalTrials.gov study NCT06016621. IPD Sharing: NO. Countries: 1. Publications: 46.
Improvisational Dance for Parkinson Disease
ClinicalTrials.gov study NCT04354298. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Effects of Clinical Music Improvisation on Resiliency in Adults Undergoing Infusion Therapy
ClinicalTrials.gov study NCT02261558. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Examining the Effects of an Improvisation Group
ClinicalTrials.gov study NCT03147924. IPD Sharing: NO. Countries: 1. Publications: 15.
Improvised Music to Enhance Intensive Interaction Version 1
ClinicalTrials.gov study NCT03188016. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluating Improvised Chest Drainage Techniques in Conflict Zones
ClinicalTrials.gov study NCT06683976. IPD Sharing: NO. Countries: 1. Publications: 0.
Impact of Music Improvisation Training on Cognitive Function in Older Adults
ClinicalTrials.gov study NCT05980286. IPD Sharing: NO. Countries: 1. Publications: 0.
Quadrophonic Recordings of free improvisation by the MS&I workshop using alps-autopan Max patch
<p>XXXXXXXXXXXXX<br> XX READ ME: X<br> XXXXXXXXXXXXX</p> <p>The audio-files in these subfolders are MP3 - stems for a 4 channel surround sound system.</p> <p>These recordings have been left unedited due to their academic nature, demonstrating the Autopan Max Patch <a href="https://doi.org/10.5281/zenodo.5607120">(https://doi.org/10.5281/zenodo.5607120)</a> in conjunction with the Acoustic Localisation Positioning System (ALPS) algorithm <a href="https://doi.org/10.5281/zenodo.5602869">(https://doi.org/10.5281/zenodo.5602869</a>). They are not intended to be played as musical works in their own rights. For the recording, the loudspeakers were at equal distance within a 3 m radius. The files sound best if played back on loudspeakers in a similar arrangement.Due to their raw nature, THERE MAY BE CLICKS AND LOUD PEAKS TO BE AWARE OF DURING PLAYBACK!! Also, Although acoustic feedback on these recordings are intentional, they are not level-limited, please be aware.</p> <p>We also would like to underline that these are recordings of a free interdisciplinary improvisation, so only form one aspect of a participatory practice.</p> <p>On behalf of the MS&I Collective, Dominik Schlienger 2021</p>
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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.