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).
Highlights
Deep learning is mainly based on accurate understanding, in-depth exploration, and reflection and driven by learners' intrinsic motivation, to critically and autonomously learn new knowledge and new ideas, solve practical problems, and cultivate students' deep learning and exploration capabilities [1]. is model has an important role in promoting the cultivation of students’ abilities, the shaping of students’ character, and the improvement of students’ quality [2].erefore, in junior high school music education teaching, teachers should reasonably use the deep learning model to improve students’ comprehensive music ability [3]
Deep learning is a field of machine learning which is inspired by a neural structure [4]. ese networks extract the features automatically from the dataset and are capable of learning any nonlinear function. at is why neural networks are called as universal functional approximators [5]
In the middle school music classroom, if teachers teach students the basic knowledge and skills of music, they will not be able to help students grasp the connotation of music comprehensively and accurately, and they will not be able to realize the second degree of creation of music works. erefore, teachers should guide students to actively participate in music activities and to learn in-depth in classroom teaching, so that they can develop students’ music appreciation ability, improve their music literacy, enable them to form good creative ability, appreciation ability, and expression ability in music, effectively cultivate students’ aesthetic interest, Mobile Information Systems and enable them to form an optimistic and upward-looking attitude towards life and to love life and expression more
Summary
Received 14 October 2021; Revised 5 November 2021; Accepted 11 November 2021; Published 2 December 2021. 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 help students master more music knowledge and improve their music skills and cultivate students’ music literacy and enhance their music ability (Swiechowski, 2015). Erefore, in junior high school music teaching, teachers should actively apply the deep learning model and improve the teaching level and comprehensively cultivate students’ music literacy (Whitenack and Swanson, 2003). Is 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) 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. is 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)
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