Generation of sports highlights using a combination of supervised & unsupervised learning in audio domain

In our past work we have used supervised audio classification to develop a common audio-based platform for highlight extraction that works across three different sports. We then use a heuristic to post-process the classification results to identify interesting events and also to adjust the summary length. In this paper, we propose a combination of unsupervised and supervised learning approaches to replace the heuristic. The proposed unsupervised framework mines the semantic audio-visual labels so as to detect "interesting" events. We then use a hidden Markov model based approach to control the length of the summary. Our experimental results show that the proposed techniques are promising.