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The Use of Deep Learning-Based Intelligent Music Signal Identification and Generation Technology in National Music Teaching

The research expects to explore the application of intelligent music recognition technology in music teaching. Based on the Long Short-Term Memory network knowledge, an algorithm model which can distinguish various music signals and generate various genres of music is designed and implemented. First...

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Autores principales: Tang, Hui, Zhang, Yiyao, Zhang, Qiuying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9257106/
https://www.ncbi.nlm.nih.gov/pubmed/35814087
http://dx.doi.org/10.3389/fpsyg.2022.762402
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author Tang, Hui
Zhang, Yiyao
Zhang, Qiuying
author_facet Tang, Hui
Zhang, Yiyao
Zhang, Qiuying
author_sort Tang, Hui
collection PubMed
description The research expects to explore the application of intelligent music recognition technology in music teaching. Based on the Long Short-Term Memory network knowledge, an algorithm model which can distinguish various music signals and generate various genres of music is designed and implemented. First, by analyzing the application of machine learning and deep learning in the field of music, the algorithm model is designed to realize the function of intelligent music generation, which provides a theoretical basis for relevant research. Then, by selecting massive music data, the music style discrimination and generation model is tested. The experimental results show that when the number of hidden layers of the designed model is 4 and the number of neurons in each layer is 1,024, 512, 256, and 128, the training result difference of the model is the smallest. The classification accuracy of jazz, classical, rock, country, and disco music types can be more than 60% using the designed algorithm model. Among them, the classification effect of jazz schools is the best, which is 77.5%. Moreover, compared with the traditional algorithm, the frequency distribution of the music score generated by the designed algorithm is almost consistent with the spectrum of the original music. Therefore, the methods and models proposed can distinguish music signals and generate different music, and the discrimination accuracy of different music signals is higher, which is superior to the traditional restricted Boltzmann machine method.
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spelling pubmed-92571062022-07-07 The Use of Deep Learning-Based Intelligent Music Signal Identification and Generation Technology in National Music Teaching Tang, Hui Zhang, Yiyao Zhang, Qiuying Front Psychol Psychology The research expects to explore the application of intelligent music recognition technology in music teaching. Based on the Long Short-Term Memory network knowledge, an algorithm model which can distinguish various music signals and generate various genres of music is designed and implemented. First, by analyzing the application of machine learning and deep learning in the field of music, the algorithm model is designed to realize the function of intelligent music generation, which provides a theoretical basis for relevant research. Then, by selecting massive music data, the music style discrimination and generation model is tested. The experimental results show that when the number of hidden layers of the designed model is 4 and the number of neurons in each layer is 1,024, 512, 256, and 128, the training result difference of the model is the smallest. The classification accuracy of jazz, classical, rock, country, and disco music types can be more than 60% using the designed algorithm model. Among them, the classification effect of jazz schools is the best, which is 77.5%. Moreover, compared with the traditional algorithm, the frequency distribution of the music score generated by the designed algorithm is almost consistent with the spectrum of the original music. Therefore, the methods and models proposed can distinguish music signals and generate different music, and the discrimination accuracy of different music signals is higher, which is superior to the traditional restricted Boltzmann machine method. Frontiers Media S.A. 2022-06-22 /pmc/articles/PMC9257106/ /pubmed/35814087 http://dx.doi.org/10.3389/fpsyg.2022.762402 Text en Copyright © 2022 Tang, Zhang and Zhang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Tang, Hui
Zhang, Yiyao
Zhang, Qiuying
The Use of Deep Learning-Based Intelligent Music Signal Identification and Generation Technology in National Music Teaching
title The Use of Deep Learning-Based Intelligent Music Signal Identification and Generation Technology in National Music Teaching
title_full The Use of Deep Learning-Based Intelligent Music Signal Identification and Generation Technology in National Music Teaching
title_fullStr The Use of Deep Learning-Based Intelligent Music Signal Identification and Generation Technology in National Music Teaching
title_full_unstemmed The Use of Deep Learning-Based Intelligent Music Signal Identification and Generation Technology in National Music Teaching
title_short The Use of Deep Learning-Based Intelligent Music Signal Identification and Generation Technology in National Music Teaching
title_sort use of deep learning-based intelligent music signal identification and generation technology in national music teaching
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9257106/
https://www.ncbi.nlm.nih.gov/pubmed/35814087
http://dx.doi.org/10.3389/fpsyg.2022.762402
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