[1]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(2):196-201.[doi:10.11992/tis.201612012]
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Recognition of difficulty level of piano score based on metric learning support vector machine
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
13
Number of periods:
2018 2
Page number:
196-201
Column:
学术论文—机器学习
Public date:
2018-04-15
- Title:
- Recognition of difficulty level of piano score based on metric learning support vector machine
- Keywords:
- digital piano score; recognition of difficulty level; classification algorithm; support vector machine; metric learning; Gauss radial basis kernel function
- CLC:
- TP391.4
- DOI:
- 10.11992/tis.201612012
- 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.
- References:
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