SVM-based audio classification for instructional video analysis

Abstract

Automatic content analysis and annotation for efficient search and browsing of topics in instructional videos are current challenges in the management of e-learning content repositories. This paper presents our current work on classifying the soundtrack of instructional videos into seven distinct audio classes using the Support Vector Machine (SVM) technology. The classification results are then used to partition a video into homogeneous audio segments, which forms the fundamental basis for its higher-level content analysis and exploration, Initial experiments carried out on three education and four training videos totalling to 185 minutes have yielded an average 97.9% classification accuracy. The performance comparisons between the SVM-based, the decision tree (DT)-based and the threshold-based audio classification schemes further demonstrates the superiority of the proposed scheme.