基于熵和支持向量机的音乐分类方法  

Music Classification Method Based on Entropy and Support Vector Machine

作　　者：高林杰[1] 张明[1] 

机构地区：[1]上海海事大学信息工程学院,上海201306

出　　处：《计算机系统应用》2014年第5期83-88,共6页Computer Systems & Applications

摘　　要：音乐分类研究已经持续多年,但目前检索效率并不理想.提出了一种基于熵和支持向量机的音乐分类方法.利用滤波器把音乐片段分解成不同的频率通道,然后通过离散傅里叶变换转换为频谱图后计算信息熵,并使用支持向量机在四个类别的音乐集上进行训练和测试.同时,比较了三种不同的滤波器,其中Bark滤波取得了80%的识别率,实验结果表明其比使用MFCC特征分类效果要好.Research on music classification has been processing years, but the performance of each method is not very well. This paper proposes a new method based on entropy and support vector machine for music classification. It uses bank of filters to decompose the music clip into different channels. Then the filters turns it into spectrum through discrete Fourier transform and compute the information entropy and uses support vector machine training and testing on a dataset containing four categories of music. The experiment compares three different kinds of filters, among which the Bark filter achieves an accuracy of 80%. The result shows that the proposed feature vector is better than MFCC.

关 键 词：音乐分类 滤波器 频谱图 信息熵 支持向量机 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]