A Study of Audio Classification on Using Different Feature Schemes with Three Classifiers
Audio classification is an important tool used in audio retrieval and other audio processing application areas. In this paper, five kinds of audio data including speech, symphony, jazz, light music and concerto are studied. Audio features are extracted by audio analysis, and formed into different feature sets. The performance on applying Support Vector Machine, Fisher Kernel Classifier and Potential Function Classifier to different audio feature sets is then examined. Finally, this paper presents the results of a number of experiments, which attempt to increase the classification accuracy by combining three classifiers and different feature sets. The classification accuracy can achieve 97％ by using a combination of feature set 1, set 2, set 3, and set 5 with Support Vector Machine and Fisher Kernel Classifier, the accuracy is close or better than the ones reported on the similar data sets and using other classifiers. (Abstract)
feature extraction feature set support vector machine fisher kernel potential function (keywords)
Yan Feng Huijing Dou Yanzhou Qian
School of Electronic Information and Control Engineering,Beijing University of Technology Beijing 100124,China
国际会议
昆明
英文
298-302
2010-10-17（万方平台首次上网日期，不代表论文的发表时间）