CASEMANS 2009: the 3rd ACM International Workshop on Context-Awareness for Self-Managing Systems
Proceedings of the 3rd ACM International Workshop on Context-Awareness for Self-Managing Systems
Proceedings of the 3rd ACM International Workshop on Context-Awareness for Self-Managing Systems
Enabling group-awareness through context-based service provisioning
Proceedings of the 3rd ACM International Workshop on Context-Awareness for Self-Managing Systems
Adaptive audio-based context recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Researchers have employed audio features to capture complex human settings. Most approaches model a complex setting as a monolithic scene; that is, they consider the stochastic property of the audio signal representing a setting as a whole, not an aggregation of distinct scenes. So, when some aspects of the training data are missing or are weakly represented in the test signal, recognition schemes trained to recognize the setting often make erroneous conclusions. Moreover, these approaches make it difficult to declaratively define new settings by combining scenes. A proposed conceptual architecture enables recognition of complex settings by combining scenes. The associated architecture and modeling approach help achieve human-like reasoning and improve recognition accuracy. The authors demonstrate their approach by modeling seven everyday settings with 27 atomic scenes.