摘要
传统的自动在线记谱方法存在记谱速率小的缺陷,为此提出基于大数据的自动在线记谱方法研究。采用回声器设备对音乐信号进行采集,得到音乐信号集合,采用快速傅里叶变换方法对音乐信号集合进行预处理,并对基频特征进行提取,将提取的基频特征转换为音符,采用能量基音符切分算法对音符进行切分,采用乐谱识别算法对音符切分结果进行乐谱识别,得到乐谱信息,利用大数据技术对乐谱进行存储与记忆,实现了乐谱的自动在线记忆。通过实验可得,提出的自动在线记谱方法记谱速率比传统方法高出25.8%,说明提出的自动在线记谱方法具备极高的有效性。
The traditional automatic on-line spectral recording method has the shortcoming of slow recording speed,so this paper proposes an automatic on-line spectral recording method based on large data is proposed in this paper.The music signal set is acquired by using the echo equipment,and the music signal set is preprocessed by the fast Fourier transform method,and the fundamental frequency features are extracted.The extracted fundamental frequency features are converted into notes.The energy pitch segmentation algorithm is used to segment the notes,and the music score recognition algorithm is used to recognize the results of the musical note segmentation.The information of the music score is obtained,and the large data technology is used to store and memorize the music score,which realizes the automatic online memory of the music score.The experimental results show that the proposed automatic on-line spectral recording method has a 25.8% higher spectral recording rate than the traditional method,which shows that the proposed automatic on-line spectral recording method has a very high efficiency.
作者
党焱
DANG Yan(Shanxi Polytechnic Institute,Xianyang Shanxi 712000,China)
出处
《自动化与仪器仪表》
2019年第10期30-33,共4页
Automation & Instrumentation
基金
陕西工业职业技术学院2018院级科研计划项目《音乐教育在高职院校大学生综合素质培养中的作用研究》(No.ZK18-48)
第二批陕西省社区教育实验项目《以艺术活动为载体在社区开展社会主义核心价值观教育的实验》
关键词
大数据
自动
在线
记谱
基频
音乐信号
big data
automatic
online
notation
fundamental frequency
music signal
