机器学习技术在古典音乐教育中的应用

Q2 Social Sciences
Dongfang Wang
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引用次数: 0

摘要

目的是促进中国音乐教育健康稳定发展。频域时频序列拓扑结构可以提高卷积运算的效果。因此,本文将上述算法应用于古典音乐教育,包括古典乐器识别、古典音乐特征提取与识别、古典音乐教育质量评价。根据输出结果与主观评价之间的相关性,可以判断音乐质量评价体系的好坏。相关性越高,音质评价方法越好。通过相关实验证明,DTW乐谱比对和端到端比对在古典音乐特征提取上更成功,在古典乐器识别上更准确。语音教学质量的客观评价方法比p563音乐教学质量评价更客观准确。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of Machine Learning Technology in Classical Music Education
The goal is to promote the healthy and stable development of music education in China. The time-frequency sequence topology in frequency domain can improve the effect of convolution operation. Therefore, this paper applies the above algorithms to classical music education, including the recognition of classical instruments, the feature extraction and recognition of classical music, and the quality evaluation of classical music education. The quality of the music quality evaluation system can be judged according to the correlation between the output results and the subjective evaluation. The higher the correlation, the better the music quality evaluation method. Through relevant experiments, it is proved that DTW score alignment and end-to-end are more successful in extracting the features of classical music, and more accurate in identifying classical instruments. The objective evaluation method of pronunciation teaching quality is more objective and accurate than P.563 music teaching quality evaluation.
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CiteScore
2.40
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