Audio classification in a weighted SVM

Abstract

This paper presents a novel audio classification algorithm, which combines the rule-based with model-based method in an efficient way. First, the threshold-based method is performed over each audio clip for preclassification, with three typical features utilized and majority rule applied. Next, a weighted frame-based Support Vector Machine (SVM) is presented for further classification, using a new feature Mel-ICA as classification feature and preclassification results as weights. Finally, the experimental results have shown that the presented algorithm achieved effective audio classification, with accuracy rate increased greatly, and the new Mel-ICA was more suitable for classification than traditional mel-frequency cepstral coefficients (MFCCs).