Abstract
We propose a smart sound sensor for building context-aware systems that instantly learn and detect events from various kinds of everyday sounds and environmental noise by using small and low-cost device. The proposed system automatically analyzes and selects an appropriate sound recognition process, using sample sounds and a parameter templates database in the event learning phase. A user is only required to input target event sounds from a microphone or sound files. Using the proposed sensor, the developer of ubiquitous service can easily utilize real world sounds as event triggers to control appliances or human’s activity monitors for presence services without a signal processing programming.
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References
Crossbow Technology, MOTE, http://www.xbow.com/
Myers, C.S., Rabiner, L.R.: A comparative study of several dynamic time-warping algorithms for connected word recognition. IEEE Trans. on ASSP 26(3), 351–363 (1981)
Tapia, E.M., Intille, S.S., Lopez, L., Larson, K.: The design of a portable kit of wireless sensors for naturalistic data collection, Pervasive 2006, pp. 117–134 (2006)
Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Communications of the ACM 18(11), 613–620 (1975)
Sakoe, H., Chiba, S.: A Dynamic Programming Algorithm Optimization for Spoken Word Recognition. IEEE Trans. on ASSP 26(27), 43–49 (1978)
Chen, J., Kam, A.H., Zhang, J., Liu, N., Shue, L.: Bathroom Activity Monitoring Based on Sound, Pervasive 2005, pp. 47–61 (2005)
Bao, L., Intille, S.S.: Activity Recognition from User-Annotated Acceleration Data, Pervasive 2004, pp. 1–17 (2004)
Ma, L., Smith, D., Milner, B.: Environmental Noise Classification for Context-Aware Applications, Database and Expert Systems Applications, pp. 360–370 (2003)
Microchip Technology Inc, dsPIC Digital Signal Controllers, http://www.microchip.com
Lukowicz, P., Ward, J.A., Junker, H., Stager, M., Troster, G., Atrash, A., Starner, T.: Recognizing Workshop Activity Using Body Worn Microphones and Accelerometers, Pervasive 2004, pp. 18–32 (2004)
Gray, R.M.: Vector Quantization. IEEE ASSP Magazine 1, 4–29 (1984)
Cotterell, S., Mannion, R., Vahid, F., Hsieh, H.: eBlocks - An Enabling Technology for Basic Sensor Based Systems, IPSN Track on Sensor Platform, Tools and Design Methods for Networked Embedded Systems, SPOTS (2005)
Linde, Y., Buzo, A., Gray, R.M.: An Algorithm for Vector Quantizer Design. IEEE Trans. on Comm 28(1), 84–95 (1980)
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Negishi, Y., Kawaguchi, N. (2007). Instant Learning Sound Sensor: Flexible Real-World Event Recognition System for Ubiquitous Computing. In: Ichikawa, H., Cho, WD., Satoh, I., Youn, H.Y. (eds) Ubiquitous Computing Systems. UCS 2007. Lecture Notes in Computer Science, vol 4836. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76772-5_6
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DOI: https://doi.org/10.1007/978-3-540-76772-5_6
Publisher Name: Springer, Berlin, Heidelberg
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