Proposal of Instant Learning Sound Sensor for Easy Building of Real World Event Recognition System

Over the last few years, various context-sensing methods by signal-analysis of sounds and acceleration patterns in real-world events have been developed. However, it is not easy for everyone to utilize real world event recognitions by signal-analysis in developments of ubicomp applications. In this paper, we propose a smart sensor which can easily and less costly utilize a sound event recognition. We also propose an Instant Learning method which automatically configures an appropriate set of parameters in a DP matching based recognition process for the target event by evaluating several combinations of parameters. Based on our proposal, we designed and implemented an Instant Learning Sound Sensor. By evaluation experiment, we confirmed the smart sensor can automatically choose a proper set of parameters for various sound event with almost over 80% accuracy. Furthermore, we evaluated the required processing power and the memory consumption, and confirmed that the recognition process can be implemented on an one-chip microcontroller.

[1]  Andy Hopper,et al.  The Anatomy of a Context-Aware Application , 1999, Wirel. Networks.

[2]  Paul Lukowicz,et al.  Recognizing Workshop Activity Using Body Worn Microphones and Accelerometers , 2004, Pervasive.

[3]  Bernt Schiele,et al.  Analyzing features for activity recognition , 2005, sOc-EUSAI '05.

[4]  Ling Bao,et al.  Activity Recognition from User-Annotated Acceleration Data , 2004, Pervasive.

[5]  Feng Zhao,et al.  Collaborative In-Network Processing for Target Tracking , 2003, EURASIP J. Adv. Signal Process..

[6]  Vesa T. Peltonen,et al.  Computational auditory scene recognition , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[7]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[8]  Alex Pentland,et al.  Auditory Context Awareness via Wearable Computing , 1998 .

[9]  A. Harter,et al.  The Anatomy of a ContextAware Application , 1999, MobiCom 1999.

[10]  Paul Lukowicz,et al.  Sampling frequency, signal resolution and the accuracy of wearable context recognition systems , 2004, Eighth International Symposium on Wearable Computers.

[11]  Mike Y. Chen,et al.  Tracking Free-Weight Exercises , 2007, UbiComp.

[12]  R. Gray,et al.  Vector quantization , 1984, IEEE ASSP Magazine.

[13]  Albrecht Schmidt,et al.  There is more to context than location , 1999, Comput. Graph..

[14]  James Church,et al.  Wearable sensor badge and sensor jacket for context awareness , 1999, Digest of Papers. Third International Symposium on Wearable Computers.

[15]  Gregory D. Abowd,et al.  Towards a Better Understanding of Context and Context-Awareness , 1999, HUC.

[16]  Frank Vahid,et al.  eBlocks - an enabling technology for basic sensor based systems , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[17]  S. Chiba,et al.  Dynamic programming algorithm optimization for spoken word recognition , 1978 .

[18]  Robert M. Gray,et al.  An Algorithm for Vector Quantizer Design , 1980, IEEE Trans. Commun..

[19]  Ning Liu,et al.  Bathroom Activity Monitoring Based on Sound , 2005, Pervasive.