Audio classification is very important in multimedia retrieval such as audio indexing, analysis and content-based video retrieval. In this paper, we have proposed a clip-based support vector machine (SVM) approach to classify audio signals into six classes, which are pure speech, music, silence, environmental sound, speech with music and speech with environmental sound. The classification results are then used to partition a video into homogeneous audio segments, which is used to analyze and retrieve its higher-level content. The experimental results show that the proposed system not only improves classification accuracy, but also performs better than the other classification systems using the decision tree (DT), K Nearest Neighbor (K-NN) and Neural Network (NN). © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Zhu, Y., Ming, Z., & Huang, Q. (2007). SVM-based audio classification for content-based multimedia retrieval. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4577 LNCS, pp. 474–482). Springer Verlag. https://doi.org/10.1007/978-3-540-73417-8_56
Mendeley helps you to discover research relevant for your work.