[1]郭龙伟,关欣,李锵.基于测度学习支持向量机的钢琴乐谱难度等级识别[J].智能系统学报,2018,13(02):196-201.[doi:10.11992/tis.201612012]
 GUO Longwei,GUAN Xin,LI Qiang.Recognition of difficulty level of piano score based on metric learning support vector machine[J].CAAI Transactions on Intelligent Systems,2018,13(02):196-201.[doi:10.11992/tis.201612012]
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基于测度学习支持向量机的钢琴乐谱难度等级识别(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第13卷
期数:
2018年02期
页码:
196-201
栏目:
出版日期:
2018-04-15

文章信息/Info

Title:
Recognition of difficulty level of piano score based on metric learning support vector machine
作者:
郭龙伟 关欣 李锵
天津大学 电子信息工程学院, 天津 300072
Author(s):
GUO Longwei GUAN Xin LI Qiang
Department of Electronic Information Engineering, Tianjin University, Tianjin 300072, China
关键词:
数字钢琴乐谱难度等级识别分类算法支持向量机测度学习高斯径向基核函数
Keywords:
digital piano scorerecognition of difficulty levelclassification algorithmsupport vector machinemetric learningGauss radial basis kernel function
分类号:
TP391.4
DOI:
10.11992/tis.201612012
摘要:
现有钢琴乐谱难度分类主要由人工方式完成,效率不高,而自动识别乐谱难度等级的算法对类别的拟合度较低。因此,与传统将乐谱难度等级识别归结为回归问题不同,本文直接将其建模为基于支持向量机的分类问题。并结合钢琴乐谱分类主观性强、特征之间普遍存在相关性等特点,利用测度学习理论有难度等级标签乐谱的先验知识,依据特征对难度区分的贡献度,改进高斯径向基核函数,从而提出一种测度学习支持向量机分类算法——ML-SVM算法。在9类和4类难度两个乐谱数据集上,我们将ML-SVM算法与逻辑回归,基于线性核函数、多项式核函数、高斯径向基核函数的支持向量机算法以及结合主成分分析的各个支持向量机算法进行了对比,实验结果表明我们提出算法的识别正确率优于现有算法,分别为68.74%和84.67%。所提算法有效提高了基于高斯径向基核函数支持向量机算法在本应用问题中的分类性能。
Abstract:
The existing classification work about piano score’s level is mainly done manually and inefficient, while the algorithm automatically recognizing the difficulty class of music scopre has a low classification fitting degree. Therefore, different from the traditional method that takes the recognition for the difficulty class of music scope as a regression issue, the paper directly modelled it as a classification based on the support vector machine, in addition, in combination with such characteristics of the score classification as intense subjectivity and common dependency among features, the metric learning theory was utilized. The prior knowledge of the score with difficult level tag was sufficiently utilized, according to the contribution of feature in difficulty distinguishment, the Gauss radial basis kernel function was improved, so as to propose a kind of metric learning support vector machine classification algorithm —ML-SVM algorithm. In the score datasets with level 9 and level 4 difficulty, ML-SVM algorithm was compared with logistic regression, the support vector machine algorithm based on linear kernel function, polynomial kernel function, Gauss radical basis (GRB) kernel function, and various support vector machine algorithms combining principal component analysis. The results show that the proposed algorithm is much more accurate than the existing algorithms, reaching the accuracy rate 68.74% and 84.67% respectively. The proposed algorithm effectively improves the classification performance of SVM algorithm based on GRB kernel function in this application.

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备注/Memo

备注/Memo:
收稿日期:2016-12-08。
基金项目:国家自然科学基金项目(60802049,61471263);天津市自然科学基金重点项目(16JCZDJC31100).
作者简介:郭龙伟,男,1990年生,硕士研究生,主要研究方向为音乐信息检索;关欣,女,1977年生,研究员,主要研究方向为音乐信息检索、统计学习、凸优化理论和音乐信号处理;李锵,男,1974年生,教授,博士生导师,主要研究方向为医学图像处理、智能信息处理、滤波器设计、数字系统和微系统设计。发表学术论文30余篇,出版专著和教材8部。
通讯作者:关欣.E-mail:guanxin@tju.edu.cn.
更新日期/Last Update: 1900-01-01