Audio keywords generation for sports video analysis
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Tactile and visual alerts for deaf people by mobile phones
Proceedings of the 11th international ACM SIGACCESS conference on Computers and accessibility
MoVi: mobile phone based video highlights via collaborative sensing
Proceedings of the 8th international conference on Mobile systems, applications, and services
Multimedia data mining: state of the art and challenges
Multimedia Tools and Applications
A multi-class method for detecting audio events in news broadcasts
SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
Summarizing sporting events using twitter
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
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In this paper, we present an audio-based event detection algorithm shown to be effective when applied to Soccer video. The main benefit of this approach is the ability to recognise patterns that display high levels of crowd response correlated to key events. The soundtrack from a Soccer sequence is first parameterised using Mel-frequency Cepstral coefficients. It is then segmented into homogenous components using a windowing algorithm with a decision process based on Bayesian model selection. This decision process eliminated the need for defining a heuristic set of rules for segmentation. Each audio segment is then labelled using a series of Hidden Markov model (HMM) classifiers, each a representation of one of 6 predefined semantic content classes found in Soccer video. Exciting events are identified as those segments belonging to a crowd cheering class. Experimentation indicated that the algorithm was more effective for classifying crowd response when compared to traditional model-based segmentation and classification techniques.