Semantic Segmentation of Radio Programs Using Social Network Analysis and Duration Distribution Modeling
This work presents and compare two approaches for the semantic segmentation of broadcast news: the first is based on Social Network Analysis, the second is based on Poisson Stochastic Processes. The experiments are performed over 27 hours of material: preliminary results are obtained by addressing the problem of splitting different episodes of the same program into two parts corresponding to a news bulletin and a talk-show respectively. The results show that the transition point between the two parts can be detected with an average error of around three minutes, i.e. roughly 5 percent of each episode duration.
- URL: http://publications.idiap.ch/downloads/papers/2007/vinciarelli-icmevincia-2007.pdf
- Related documents: http://publications.idiap.ch/index.php/publications/showcite/vinciarelli:rr06-75
Record created on 2010-02-11, modified on 2016-08-08