Cargando…
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...
Autores principales: | , , |
---|---|
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 |
_version_ | 1784741266557239296 |
---|---|
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. |
format | Online Article Text |
id | pubmed-9257106 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT tanghui theuseofdeeplearningbasedintelligentmusicsignalidentificationandgenerationtechnologyinnationalmusicteaching AT zhangyiyao theuseofdeeplearningbasedintelligentmusicsignalidentificationandgenerationtechnologyinnationalmusicteaching AT zhangqiuying theuseofdeeplearningbasedintelligentmusicsignalidentificationandgenerationtechnologyinnationalmusicteaching AT tanghui useofdeeplearningbasedintelligentmusicsignalidentificationandgenerationtechnologyinnationalmusicteaching AT zhangyiyao useofdeeplearningbasedintelligentmusicsignalidentificationandgenerationtechnologyinnationalmusicteaching AT zhangqiuying useofdeeplearningbasedintelligentmusicsignalidentificationandgenerationtechnologyinnationalmusicteaching |