Content-Based Classification, Search, and Retrieval of Audio
IEEE MultiMedia
Using structure patterns of temporal and spectral feature in audio similarity measure
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Dominant feature vectors based audio similarity measure
PCM'04 Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing - Volume Part II
Clip-based similarity measure for query-dependent clip retrieval and video summarization
IEEE Transactions on Circuits and Systems for Video Technology
Learning to rank videos personally using multiple clues
Proceedings of the ACM International Conference on Image and Video Retrieval
Audio retrieval by segment-based manifold-ranking
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
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This paper proposes a new approach for the similarity measure and ranking of audio clips by graph modeling and matching. Instead of using frame-based or salient-based features to measure the acoustical similarity of audio clips, segment-based similarity is proposed. The novelty of our approach lies in two aspects: segment-based representation, and the similarity measure and ranking based on four kinds of similarity factors. In segmentbased representation, segments not only capture the change property of audio clip, but also keep and present the change relation and temporal order of audio features. In the similarity measure and ranking, four kinds of similarity factors: acoustical, granularity, temporal order and interference are progressively and jointly measured by optimal matching and dynamic programming, which guarantee the comprehensive and sufficient similarity measure between two audio clips. The experimental result shows that the proposed approach is better than some existing methods in terms of retrieval and ranking capabilities.