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Optimization of Choreography Teaching with Deep Learning and Neural Networks
To improve the development level of intelligent dance education and choreography network technology, the research mainly focuses on the automatic formation system of continuous choreography by using the deep learning method. Firstly, it overcomes the technical difficulty that the dynamic segmentatio...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9357785/ https://www.ncbi.nlm.nih.gov/pubmed/35958751 http://dx.doi.org/10.1155/2022/7242637 |
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author | Zhou, Qianling Tong, Yan Si, Hongwei Zhou, Kai |
author_facet | Zhou, Qianling Tong, Yan Si, Hongwei Zhou, Kai |
author_sort | Zhou, Qianling |
collection | PubMed |
description | To improve the development level of intelligent dance education and choreography network technology, the research mainly focuses on the automatic formation system of continuous choreography by using the deep learning method. Firstly, it overcomes the technical difficulty that the dynamic segmentation and process segmentation of the automatic generation architecture in traditional choreography cannot achieve global optimization. Secondly, it is an automatic generation architecture for end-to-end continuous dance notation with access to temporal classifiers. Based on this, a dynamic time-stamping model is designed for frame clustering. Finally, it is concluded through experiments that the model successfully achieves high-performance movement time-stamping. And combined with continuous motion recognition technology, it realizes the refined production of continuous choreography with global motion recognition and then marks motion duration. This research effectively realizes the efficient and refined production of digital continuous choreography, provides advanced technical means for choreography education, and provides useful experience for school network choreography education. |
format | Online Article Text |
id | pubmed-9357785 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-93577852022-08-10 Optimization of Choreography Teaching with Deep Learning and Neural Networks Zhou, Qianling Tong, Yan Si, Hongwei Zhou, Kai Comput Intell Neurosci Research Article To improve the development level of intelligent dance education and choreography network technology, the research mainly focuses on the automatic formation system of continuous choreography by using the deep learning method. Firstly, it overcomes the technical difficulty that the dynamic segmentation and process segmentation of the automatic generation architecture in traditional choreography cannot achieve global optimization. Secondly, it is an automatic generation architecture for end-to-end continuous dance notation with access to temporal classifiers. Based on this, a dynamic time-stamping model is designed for frame clustering. Finally, it is concluded through experiments that the model successfully achieves high-performance movement time-stamping. And combined with continuous motion recognition technology, it realizes the refined production of continuous choreography with global motion recognition and then marks motion duration. This research effectively realizes the efficient and refined production of digital continuous choreography, provides advanced technical means for choreography education, and provides useful experience for school network choreography education. Hindawi 2022-07-31 /pmc/articles/PMC9357785/ /pubmed/35958751 http://dx.doi.org/10.1155/2022/7242637 Text en Copyright © 2022 Qianling Zhou et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Zhou, Qianling Tong, Yan Si, Hongwei Zhou, Kai Optimization of Choreography Teaching with Deep Learning and Neural Networks |
title | Optimization of Choreography Teaching with Deep Learning and Neural Networks |
title_full | Optimization of Choreography Teaching with Deep Learning and Neural Networks |
title_fullStr | Optimization of Choreography Teaching with Deep Learning and Neural Networks |
title_full_unstemmed | Optimization of Choreography Teaching with Deep Learning and Neural Networks |
title_short | Optimization of Choreography Teaching with Deep Learning and Neural Networks |
title_sort | optimization of choreography teaching with deep learning and neural networks |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9357785/ https://www.ncbi.nlm.nih.gov/pubmed/35958751 http://dx.doi.org/10.1155/2022/7242637 |
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