Environmental sound classification for scene recognition using local discriminant bases and HMM
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Two important action scenes detection based on probability neural networks
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
A context aware sound classifier applied to prawn feed monitoring and energy disaggregation
Knowledge-Based Systems
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Semantic-level content analysis is a crucial issue to achieve efficient content retrieval and management. We propose a hierarchical approach that models the statistical characteristics of several audio events over a time series to accomplish semantic context detection. Two stages, including audio event and semantic context modeling/testing, are devised to bridge the semantic gap between physical audio features and semantic concepts. For action movies we focused in this work, hidden Markov models (HMMs) are used to model four representative audio events, i.e. gunshot, explosion, car-braking, and engine sounds. At the semantic context level, generative (ergodic hidden Markov model) and discriminative (support vector machine, SVM) approaches are investigated to fuse the characteristics and correlations among various audio events, which provide cues for detecting gunplay and car-chasing scenes. The experimental results demonstrate the effectiveness of the proposed approaches and draw a sketch for semantic indexing and retrieval. Moreover, the differences between two fusion schemes are discussed to be the reference for future research.