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Feature Extraction and Intelligent Text Generation of Digital Music
Because the current network music operation mechanism is constantly improving and the matching of music platforms and users is poor, in this paper, the characteristics of digital music are analyzed, and the music features, rhythm, tune, intensity, and timbre with the MIDI format are extracted. Then,...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9282989/ https://www.ncbi.nlm.nih.gov/pubmed/35845909 http://dx.doi.org/10.1155/2022/7952259 |
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author | Chu, Xiaoli |
author_facet | Chu, Xiaoli |
author_sort | Chu, Xiaoli |
collection | PubMed |
description | Because the current network music operation mechanism is constantly improving and the matching of music platforms and users is poor, in this paper, the characteristics of digital music are analyzed, and the music features, rhythm, tune, intensity, and timbre with the MIDI format are extracted. Then, a music feature information extraction algorithm based on neural networks is proposed, and according to the extracted information of the music style, the B2T model is adopted for intelligent text generation. Finally, test results are given by the style matching rate and ROUGE value, which show that the model is accurate and effective for classification of music and description of related text, and the extraction of music feature information has a certain influence on its intelligent text generation. |
format | Online Article Text |
id | pubmed-9282989 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-92829892022-07-15 Feature Extraction and Intelligent Text Generation of Digital Music Chu, Xiaoli Comput Intell Neurosci Research Article Because the current network music operation mechanism is constantly improving and the matching of music platforms and users is poor, in this paper, the characteristics of digital music are analyzed, and the music features, rhythm, tune, intensity, and timbre with the MIDI format are extracted. Then, a music feature information extraction algorithm based on neural networks is proposed, and according to the extracted information of the music style, the B2T model is adopted for intelligent text generation. Finally, test results are given by the style matching rate and ROUGE value, which show that the model is accurate and effective for classification of music and description of related text, and the extraction of music feature information has a certain influence on its intelligent text generation. Hindawi 2022-07-07 /pmc/articles/PMC9282989/ /pubmed/35845909 http://dx.doi.org/10.1155/2022/7952259 Text en Copyright © 2022 Xiaoli Chu. 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 Chu, Xiaoli Feature Extraction and Intelligent Text Generation of Digital Music |
title | Feature Extraction and Intelligent Text Generation of Digital Music |
title_full | Feature Extraction and Intelligent Text Generation of Digital Music |
title_fullStr | Feature Extraction and Intelligent Text Generation of Digital Music |
title_full_unstemmed | Feature Extraction and Intelligent Text Generation of Digital Music |
title_short | Feature Extraction and Intelligent Text Generation of Digital Music |
title_sort | feature extraction and intelligent text generation of digital music |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9282989/ https://www.ncbi.nlm.nih.gov/pubmed/35845909 http://dx.doi.org/10.1155/2022/7952259 |
work_keys_str_mv | AT chuxiaoli featureextractionandintelligenttextgenerationofdigitalmusic |