Comparison of different strategies for a SVM-based audio segmentation

We compare in this paper diverse hierarchical and multi-class approaches for the speech/music segmentation task, based on Support Vector Machines, combined with a median filter post-processing. We show the effciency of kernel tuning through the novel Kernel Target Alignment criterion. Quantitative results provide an F-measure of 96.9%, that represents an error reduction of about 50% compared to the results gathered by the French ESTER evaluation campaign. We also show the relevance of the SVM with very low feature vector dimension on this task.