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基于改进的CNN和STFT的鼓乐乐谱的智能生成方法

¹College of Arts, Xiamen University, Xiamen, 361005, China. Nyt2024@163.com.

Scientific reports

|October 9, 2025

概括

这项研究介绍了一种智能音乐乐谱生成方法,使用短时间里埃变换和改进的卷积神经网络. 这种方法提高了乐谱质量和生成效率,使音乐学习者受益.

科学领域:

人工智能的人工智能
音乐信息检索音乐信息检索
信号处理信号处理

背景情况:

当前的音乐乐谱生成技术在范围和输出质量方面存在局限性.
需要先进的方法来提高生成的乐谱的准确性和多样性.

研究的目的:

提出一种智能音乐乐谱生成方法.
用先进的计算技术提高乐谱创作的质量和效率.

主要方法:

用于音乐信号的时间频率转换的短时间里埃转换 (STFT).
采用了改进的卷积神经网络 (CNN) 模型,包括标签增强和内部卷积.
将该方法应用于各种音乐数据集,以评估概括能力.

主要成果:

获得了92%的匹配率和95%的完整率.
证明得分生成时间仅为1.05秒.
显示了音乐乐谱生成效率和质量的显著改进.

结论:

拟议的智能乐谱生成方法有效地提高了生成的乐谱的质量和多样性.
该方法在不同的音乐数据集中提供了强大的概括能力.
这种方法可以通过提供绩效反和提高学习效率来帮助音乐学习者.

关键词:

在美国,CNN是CNN.改进改进是一种提高.内部卷积内部卷积标签标签标签标签音乐乐谱的生成过程在STFT,STFT和STFT中使用.

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