{"title":"Research on Music Teaching and Creation Based on Deep Learning","authors":"Mingxing Liu","doi":"10.1155/2021/1738104","DOIUrl":null,"url":null,"abstract":"Under the background of quality education, music learning is also changing, from the original shallow learning to deep learning gradually. In-depth learning is a new teaching concept, which pays full attention to students’ perception and exploration of music so that students can fully experience the charm of music. It can not only help students master more music knowledge and improve their music skills but also cultivate students’ music literacy and enhance their music ability (Świechowski, 2015). Therefore, in junior high school music teaching, teachers should actively apply the deep learning model and then improve the teaching level and comprehensively cultivate students’ music literacy (Whitenack and Swanson, 2003). In this paper, two convolution-based deep learning models, Breath1d and Breath2d, were designed and constructed, and a multilayer perceptron (MLP) was used as a benchmark method for performance evaluation, and a long short-term memory (LSTM) network is applied for the classification task. This paper discusses the value and application strategies of deep learning in junior high school music teaching and hopes to provide some reference for all educational colleagues (Zhang and Nauman 2020).","PeriodicalId":18790,"journal":{"name":"Mob. Inf. Syst.","volume":"26 1","pages":"1738104:1-1738104:7"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mob. Inf. Syst.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1155/2021/1738104","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Under the background of quality education, music learning is also changing, from the original shallow learning to deep learning gradually. In-depth learning is a new teaching concept, which pays full attention to students’ perception and exploration of music so that students can fully experience the charm of music. It can not only help students master more music knowledge and improve their music skills but also cultivate students’ music literacy and enhance their music ability (Świechowski, 2015). Therefore, in junior high school music teaching, teachers should actively apply the deep learning model and then improve the teaching level and comprehensively cultivate students’ music literacy (Whitenack and Swanson, 2003). In this paper, two convolution-based deep learning models, Breath1d and Breath2d, were designed and constructed, and a multilayer perceptron (MLP) was used as a benchmark method for performance evaluation, and a long short-term memory (LSTM) network is applied for the classification task. This paper discusses the value and application strategies of deep learning in junior high school music teaching and hopes to provide some reference for all educational colleagues (Zhang and Nauman 2020).
在素质教育的背景下,音乐学习也在发生变化,由原来的浅层学习逐渐向深度学习转变。深度学习是一种新的教学理念,它充分重视学生对音乐的感知和探索,让学生充分体验音乐的魅力。它不仅可以帮助学生掌握更多的音乐知识,提高音乐技能,还可以培养学生的音乐素养,增强学生的音乐能力(Świechowski, 2015)。因此,在初中音乐教学中,教师应积极运用深度学习模式,进而提高教学水平,全面培养学生的音乐素养(Whitenack and Swanson, 2003)。本文设计并构建了两个基于卷积的深度学习模型Breath1d和Breath2d,并采用多层感知器(MLP)作为性能评估的基准方法,采用长短期记忆(LSTM)网络进行分类任务。本文探讨了深度学习在初中音乐教学中的价值和应用策略,希望能为所有教育同仁提供一些参考(Zhang and Nauman 2020)。