Combined Supervised and Unsupervised Approaches for Automatic Segmentation of Radiophonic Audio Streams

Gaël Richard, Mathieu Ramona, Slim Essid

Abstract: Speech/music discrimination is one of the most studied topics in the domain of audio data segmentation. In this paper, we propose and evaluate a novel method that includes feature selection and a combined supervised and unsupervised strategy for audio streams segmentation. A number of alternatives solutions for each component are assessed and the optimized system is compared to the approaches proposed in the framework of the ESTER campaign.