计算机技术与发展
計算機技術與髮展
계산궤기술여발전
COMPUTER TECHNOLOGY AND DEVELOPMENT
2015年
6期
166-170
,共5页
音乐情感分类%回归分析%k平面分段回归%支持向量回归
音樂情感分類%迴歸分析%k平麵分段迴歸%支持嚮量迴歸
음악정감분류%회귀분석%k평면분단회귀%지지향량회귀
music emotion classification%regression analysis%k-plane piecewise regression%support vector regression
为了提高基于回归的音乐情感分类准确率,文中运用了k平面分段回归的方法,在音乐特征与音乐情感组成的高维空间内,通过多次迭代寻找超平面的方法直接求解非线性回归问题,进而预测二维情感变量值Valence与Arousal,并通过该二维情感变量值进行音乐情感分类。为了验证分类系统的性能,实验中按MIREX分类标准建立有5类音乐情感的音乐库,对其300首音乐样本进行分类,与传统的多元线性回归和支持向量回归相比分类准确率有了一定提高。表明k平面分段回归的方法可以有效运用于音乐情感分类。
為瞭提高基于迴歸的音樂情感分類準確率,文中運用瞭k平麵分段迴歸的方法,在音樂特徵與音樂情感組成的高維空間內,通過多次迭代尋找超平麵的方法直接求解非線性迴歸問題,進而預測二維情感變量值Valence與Arousal,併通過該二維情感變量值進行音樂情感分類。為瞭驗證分類繫統的性能,實驗中按MIREX分類標準建立有5類音樂情感的音樂庫,對其300首音樂樣本進行分類,與傳統的多元線性迴歸和支持嚮量迴歸相比分類準確率有瞭一定提高。錶明k平麵分段迴歸的方法可以有效運用于音樂情感分類。
위료제고기우회귀적음악정감분류준학솔,문중운용료k평면분단회귀적방법,재음악특정여음악정감조성적고유공간내,통과다차질대심조초평면적방법직접구해비선성회귀문제,진이예측이유정감변량치Valence여Arousal,병통과해이유정감변량치진행음악정감분류。위료험증분류계통적성능,실험중안MIREX분류표준건립유5류음악정감적음악고,대기300수음악양본진행분류,여전통적다원선성회귀화지지향량회귀상비분류준학솔유료일정제고。표명k평면분단회귀적방법가이유효운용우음악정감분류。
In this paper,a piecewise regression approach of k-plane is employed in order to improve the classification accuracy of music emotion based on regression. It solves the nonlinear regression problem directly through several iterations in high dimensional space con-sisted by music feature and music emotion,predicting the valence and arousal values in the emotion model and classifying the music emo-tion. To verify the performance of classifier,test the classifier on 300 music sample from a music dataset which is commonly employed in MIREX,and the testing results on classification accuracy of the proposed approach are compared with the results from multiple linear re-gression and support vector regression. The experimental results show that k-plane piecewise regression approach can achieve the higher accuracy than the other two. That is to say the method of k-plane piecewise regression can be effectively applied to music emotion classi-fication.
