A NOVAL AUDIO CLASSIFICATION ALGORITHM BASED ON GA AND SVM WITH COMBINED KERNEL FUNCTION

摘要：

Audio classification is an important access to extract audio structure and content, and is a premise for audio content analysis, retrieval and further treatment. Support Vector Machine (SVM) is a valid statistic learning method. In this paper, learning algorithm of SVM is introduced to construct classifier, and construct a new kernel function and use Genetic Algorithm (GA) to optimize the parameters of classifier model. This paper proposes a new audio classification algorithm, GA-CBSVM based on GA and SVM with combined kernel function to classify speech, music and their mixed audio. The experimental results show that GA-CBSVM is excellent for audio classification and the average of classification accuracy is up to 93.08％.

关键词： Audio classification Support vector machine Combined kernel function Genetic algorithm

作者: LI Jing WAN Juan ZHANG Yun-lu

作者单位: School of Computer Science, Wuhan University Wuhan, Hubei, 430079, China

会议类型: 国际会议

会议名称: 第八届分布式计算及其应用国际学术研讨会(The 8th International Symposium on Distributed Computing and Applications to Business,Engineering and Science)

会议地点: 武汉

会议语种:英文

页码: 51-53

在线出版日期: 2009-10-16（万方平台首次上网日期，不代表论文的发表时间）