Li Huizi
School of Music and Recording Arts, Communication University of China, Beijing, China.
Front Psychol. 2021 Sep 16;12:705116. doi: 10.3389/fpsyg.2021.705116. eCollection 2021.
The objective of the study was to enhance quality education in the traditional pre-school piano education. Deep Learning (DL) technology is applied to piano education of children to improve their interest in learning music. Firstly, the problems of the traditional piano education of children were analyzed with the teaching patterns discussed under educational psychology, and a targeted music education plan was established. Secondly, musical instrument recognition technology was introduced, and the musical instrument recognition model was implemented based on DL. Thirdly, the proposed model was applied to the piano education of children to guide the music learning of students and improve their interest in piano learning. The feature recognition and acquisition of the proposed model were improved. Finally, the different teaching patterns were comparatively analyzed through the Questionnaire Survey (QS). The experimental results showed that the instrument recognition accuracy of Hybrid Neural Network (HNN) is 97.2%, and with the increase of iterations, the recognition error rate of the model decreases and stabilizes. Therefore, the proposed HNN based on DL for musical instrument recognition can accurately identify musical features. The QS results showed that the introduction of musical instrument recognition technology in the piano education of children can improve their interest in piano learning. Therefore, the establishment of the piano education patterns based on the piano education model can improve the effectiveness of teaching piano to students. This research provides a reference for the intelligentization of children's piano education.
本研究的目的是提高传统学前钢琴教育中的素质教育。将深度学习（DL）技术应用于儿童钢琴教育，以提高他们对音乐学习的兴趣。首先，结合教育心理学所讨论的教学模式，分析儿童传统钢琴教育存在的问题，并制定有针对性的音乐教育计划。其次，引入乐器识别技术，并基于深度学习实现乐器识别模型。第三，将所提出的模型应用于儿童钢琴教育，以指导学生的音乐学习并提高他们对钢琴学习的兴趣。对所提出模型的特征识别与获取进行了改进。最后，通过问卷调查（QS）对不同教学模式进行比较分析。实验结果表明，混合神经网络（HNN）的乐器识别准确率为97.2%，并且随着迭代次数的增加，模型的识别错误率降低并趋于稳定。因此，所提出的基于深度学习的用于乐器识别的HNN能够准确识别音乐特征。问卷调查结果表明，在儿童钢琴教育中引入乐器识别技术可以提高他们对钢琴学习的兴趣。因此，基于钢琴教育模型建立钢琴教育模式可以提高对学生的钢琴教学效果。本研究为儿童钢琴教育的智能化提供了参考。
