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Issue No.05 - September/October (2008 vol.23)
pp: 58-65
Waltenegus Dargie , Technical University of Dresden
Tobias Tersch , Sidon Software and Engineering Service-Providing Company
ABSTRACT
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.
INDEX TERMS
audio-signal processing, context awareness, context recognition, context reasoning, smart devices, smart systems.
CITATION
Waltenegus Dargie, Tobias Tersch, "Recognition of Complex Settings by Aggregating Atomic Scenes", IEEE Intelligent Systems, vol.23, no. 5, pp. 58-65, September/October 2008, doi:10.1109/MIS.2008.90
REFERENCES
1. H.-Y. Chang et al., "Performance Improvement of Vector Quantization by Using Threshold," Advances in Multimedia Information Processing—PCM 2004, LNCS 3333, Springer, 2005, pp. 647–654.
2. V. Peltonen et al., "Computational Auditory Scene Recognition," Proc. Int'l Conf. Acoustic Speech and Signal Processing, 2002.
3. D. Heckerman, "A Tutorial on Learning with Bayesian Networks," Learning in Graphical Models, M.I. Jordan, ed., MIT Press, 1999, pp. 301–354.
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