基于测度学习支持向量机的钢琴乐谱难度等级识别

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。
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