基于支持向量机的多类音频分类  

MULTI-CLASS AUDIO CLASSIFICATION BASED ON SUPPORT VECTOR MACHINE

作　　者：俞玉莲[1] 郭世杰[2] 

机构地区：[1]上海电子信息职业技术学院通信与信息工程系,上海201411 [2]上海航天技术研究院信号处理部,上海201109

出　　处：《计算机应用与软件》2010年第4期98-101,共4页Computer Applications and Software

基　　金：上海高校选拔培养优秀青年教师科研专项基金项目(DXZ06007)

摘　　要：研究一种用支持向量机(SVM)进行多类音频分类的方法,其中引入增广两类分类法(AB法)设计多类分类器。该算法把音频分为四类:音乐、纯语音、带背景音的语音和典型的环境音,并分析了这几类音频的八个区别性特征,包括修正低能量成分比率(MLER)和修正基频(MPF)两个新特征以及频域总能量、子带能量、频率中心等其它六个基本特征,综合考察了不同特征集在基于SVM分类器中的分类精度。实验结果表明,提取的音频特征有效,基于SVM的多类音频分类效果良好。In this paper we studied a multi-class audio classification algorithm performed by the support vector machine（SVM）,in which augmented binary-class（AB） classification method was introduced to design the multi-class classifier.Four classes were considered in audio frequency：music,pure speech,speech over background and typical environment sound,and eight discriminating features of these audio classes were analysed,including the two new ones proposed by the author-modified low energy component ratio（MLER） and modified pitch frequency（MPF）,and other six basic features such as spectrum energy,sub-band powers,frequency centroid,etc.The classification accuracies of different features＇ set in SVM-based audio classifier were evaluated comprehensively.Experiment results show that the audio features selected in this paper are effective for audio classification,and the result of applying SVM to multi-class audio classification is good.

关 键 词：支持向量机 音频分类 增广两类分类法 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]