In our past work we have used supervised audio classification to develop a common audio-based platform for highlight extraction that works across three diEerent 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.

In our past work, we have attempted to use a mid-level feature namely the state population histogram obtained from the Hidden Markov Model (HMM) of a general sound class, for speaker change detection so as to extract semantic boundaries in broadcast news. In this paper, we compare the performance of our previous approach with another approach based on video shot detection and speaker change detection using the Bayesian Information Criterion (BIC). Our experiments show that the latter approach performs significantly better than the former. This motivated us to examine the mid-level feature closely. We found that the component population histogram enabled discovery of broad phonetic categories such as vowels, nasals, fricatives etc, regardless of the number of distinct speakers in the test utterance. In order for it to be useful for speaker change detection, the individual components should model the phonetic sounds of each speaker separately. From our experiments, we conclude that state/component population histograms can only be useful for further clustering or semantic class discovery if the features are chosen carefully so that the individual states represent the semantic categories of interest. SPIE Internet Multimedia Management Systems IVThis work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. ABSTRACTIn our past work, we have attempted to use a mid-level feature namely the state population histogram obtained from the Hidden Markov Model (HMM) of a general sound class, for speaker change detection so as to extract semantic boundaries in broadcast news. In this paper, we compare the performance of our previous approach with another approach based on video shot detection and speaker change detection using the Bayesian Information Criterion (BIC). Our experiments show that the latter approach performs significantly better than the former. This motivated us to examine the mid-level feature closely. We found that the component population histogram enabled discovery of broad phonetic categories such as vowels, nasals, fricatives etc, regardless of the number of distinct speakers in the test utterance. In order for it to be useful for speaker change detection, the individual components should model the phonetic sounds of each speaker separately. From our experiments, we conclude that state/component population histograms can only be useful for further clustering or semantic class discovery if...