Alle Publikationen
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2011
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(2011) : A novel real-time emotion detection system from audio streams based on Bayesian Quadratic Discriminate Classifier for ADAS: Proceedings of the Joint INDS’11 ISTET’11: Klagenfurt, Austria: IEEE, S. 1-5
DOI: https://doi.org/10.1109/INDS.2011.6024783 Abstract: This paper presents a real-time emotion recognition concept of voice streams. A comprehensive solution based on Bayesian Quadratic Discriminate Classifier(QDC) is developed. The developed system supports Advanced Driver Assistance Systems (ADAS) to detect the mood of the driver based on the fact that aggressive behavior on road leads to traffic accidents. We use only 12 features to classify between 5 different classes of emotions. We illustrate that the extracted emotion features are highly overlapped and how each emotion class is effecting the recognition ratio. Finally, we show that the Bayesian Quadratic Discriminate Classifier is an appropriate solution for emotion detection systems, where a real-time detection is deeply needed with a low number of features.
Keywords: ADAS, Advanced driver assistance systems, Angemessen(heit) (von Technik), audio signal processing, audio streaming, audio streams, Bayes methods, Bayesian methods, Bayesian quadratic discriminate classifier, driver information systems, emotion feature extraction, emotion recognition, Feature extraction, ieee xplore, real-time emotion detection system, real-time emotion recognition concept, road accidents, speech, speech recognition, Support vector machines, traffic accidents, Vehicles, voice streams 2005
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(2005): Active affective State detection and user assistance with dynamic bayesian networks. In: IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 35 (1), S. 93-105. DOI: 10.1109/TSMCA.2004.838454
DOI: https://doi.org/10.1109/TSMCA.2004.838454 Abstract: With the rapid development of pervasive and ubiquitous computing applications, intelligent user-assistance systems face challenges of ambiguous, uncertain, and multimodal sensory observations, user’s changing state, and various constraints on available resources and costs in making decisions. We introduce a new probabilistic framework based on the dynamic Bayesian networks (DBNs) to dynamically model and recognize user’s affective states and to provide the appropriate assistance in order to keep user in a productive state. We incorporate an active sensing mechanism into the DBN framework to perform purposive and sufficing information integration in order to infer user’s affective state and to provide correct assistance in a timely and efficient manner. Experiments involving both synthetic and real data demonstrate the feasibility of the proposed framework as well as the effectiveness of the proposed active sensing strategy.
Keywords: active affective state detection, active fusion, active sensing mechanism, affective state detection, Angemessen(heit) (von Technik), Bayesian methods, Bayesian networks (BNs), belief networks, Context modeling, Costs, dynamic Bayesian networks, Face detection, ieee xplore, information integration, Information theory, Intelligent networks, Intelligent sensors, Intelligent Systems, intelligent user assistance system, probabilistic framework, sensor fusion, Systems engineering, theory, Ubiquitous computing, user assistance, user interfaces
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